博士(工学), 東京大学

Ph.D. in Engineering, The University of Tokyo.

Computer Science

Artificial intelligence

Intelligent Informatic

計算機科学

人工知能

知能情報学

Informatics, Intelligent informatics

Apr. 2024 - Present
The University of Electro-Communications, Graduate School of Informatics and Engineering, 客員教員

Apr. 2024 - Present
Information Technology Center, The University of Tokyo, Interdisciplinary Information Science Research Division, Professor

May 2011 - Mar. 2024
The University of Electro-Communications, Professor

01 Apr. 2010 - 30 Apr. 2011
The University of Electro-Communications, Associate Professor

01 May 2006 - 31 Mar. 2010
The University of Electro-Communications, Associate Proffesor

01 Apr. 2002 - 30 Apr. 2006
Tokyo Institute of Technology, Lecturer

01 Oct. 1998 - 31 Mar. 2002
Advanced Telecommunications Research Institute International (ATR), Researcher

01 Apr. 1995 - 30 Sep. 1995
All Nippon Airlways (ANA)

Oct. 1995 - Sep. 1998
The University of Tokyo, Faculty of Engineering, Advanced Science and Technology

Apr. 1993 - Mar. 1995
Kyoto University, Graduate school of Engineering, 応用システム科学専攻

Apr. 1989 - Mar. 1993
Ritsumeikan University, College of Science and Engineering, 情報工学科

01 Apr. 1986 - 31 Mar. 1989
Osaka Ibaraki High School

2023 - Present
強化学習とそのハイブリッド手法の最前線 [Co-Organizer], 計測自動制御学会、システム・情報部門 学術講演会 2023 (SSI2023)

2015 - Present
プログラム委員, The International Symposium on Swarm Behavior and Bio-Inspired Robotics（SWARM）, Society

2013 - Present
委員, 宇宙航空研究開発機構（JAXA）、宇宙工学委員会

2012 - Present
プログラム委員, The International Workshop on Modern Science and Technology, Society

2012 - Present
テクニカル・プログラム委員, The International Symposium on Medical Information and Communication Technology（ISMICT）, Society

2011 - Present
委員・発起人, 計測自動制御学会，システムインテグレーション部門，スワームロボティクス調査研究会, Society

2011 - Present
副主査, 計測自動制御学会，システム・情報部門，関係論的システム科学調査研究会, Society

2011 - Present
委員, 未来研究開発検討委員会，科学技術振興機構

2009 - Present
Editorial Board, Journal of Advanced Computational Intelligence and Intelligent Informatics, Society

2007 - Present
プログラム委員, International Workshop on Learning Classifier Systems（IWLCS）、Evolutionary Machine learning（EML), Society

2007 - Present
Steering Committee, International Workshop on Multi-Agent Based Simulation（MABS）, Society

2006 - Present
委員, 計測自動制御学会知能工学部会, Society

2004 - Present
委員, 国際自動制御連盟(IFAC) 宇宙航空委員会 日本部会

2004 - Present
Board member, Chair of committee of the international relations, Pacific Asian Association for Agent-based Approach in Social Systems Sciences（PAAA), Society

2003 - Present
プログラム委員, International Symposium on Artificial Intelligence, Robotics and Automation in Space（i-SAIRAS）, Society

2002 - Present
プログラム委員, Special Issue of the Evolutionary Computation Journal on Learning Classifier Systems, Society

1999 - Present
プログラム委員, Genetic and Evolutionary Computation Conference（GECCO）, Society

1999 - Present
委員, 計測自動制御学会、システム工学部会, Society

Oct. 2023 - Sep. 2024
Co-chair, Impact of GenAI on Social and Individual Well-being, AAAI 2024 Spring Symposium

Jun. 2021 - May 2024
代議員, 人工知能学会, Society

Apr. 2023 - Mar. 2024
Guest Editor, Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII)

01 Apr. 2021 - 31 Mar. 2022
調布飛行場運用管理システムの設計・開発等業務委託技術審査委員, 調布飛行場, Government

2021
委員・発起人, 計測自動制御学会，システム・情報部門，境界と関係性を視座とするシステム学調査研究会

2004 - 2017
プログラム委員, International Workshop on Multi-Agent Based Simulation（MABS）, Society

2014 - 2016
専門委員, 電子情報通信学会 ヘルスケア・医療情報通信技術研究専門委員会

2014 - 2016
委員, 計測自動制御学会，システム・情報部門，安心・安全・快適社会実現のための新たなシステムズアプローチ調査研究会

2016
プログラム委員, International Symposium on Distributed Autonomous Robotic Systems（DARS）, Society

2014
プログラム委員, Bio-inspired Information and Communications Technolo- gies（BICT）, Society

2014
委員・発起人, 情報処理学会，情報環境領域，高齢社会デザイン研究会

2011
国際科タスクフォース委員, 計測自動制御学会, Society

2006 - 2010
プログラム委員, World Congress on Social Simulation（WCSS）, Society

2001 - 2009
プログラム委員, International Workshop on Agent-based Approaches in Economic and Social Complex Systems（AESCS）, Society

2007 - 2008
World Congress on Social Simulation（WCSS）, Co-chair, Society

2007 - 2007
Advisory committee, International Workshop on Learning Classifier Systems（IWLCS）、Evolutionary Machine learning（EML), Society

2006 - 2007
委員, 計測自動制御学会、知能工学部会, Society

2004 - 2006
Co-Chair, International Workshop on Learning Classifier Systems（IWLCS）、Evolutionary Machine learning（EML), Society

2003 - 2006
Co-Chair, International Workshop on Multi-Agent Based Simulation（MABS）, Society

2002 - 2003
Co-Chair, International Workshop on Agent-based Approaches in Economic and Social Complex Systems（AESCS）, Society

2001 - 2002
幹事, 計測自動制御学会、システム工学部会, Society

1997
Member, Institute of Electrical and Electronics Engineers, Society

1997
会員, IEEE, Society

1996
システム工学部会 委員, 計測自動制御学会, Society

1996
システム工学部会 幹事, 計測自動制御学会, Society

1994
MYCOM 副委員長, 人工知能学会, Society

1994
MYCOM プログラム実行委員, 人工知能学会, Society

1994
MYCOM Chair, The Japanese Society for Artificial Intelligence, Society

1994
MYCOM 委員長, 人工知能学会, Society

1994
人工知能学会全国大会 プログラム委員, 人工知能学会, Society

Nov. 2023
計測自動制御学会，システム・情報部門 学術講演会 2023
マルチエージェント強化学習におけるカリキュラム学習の影響分析に基づいたカリキュラム設計
［SSI研究奨励賞］, 坂上 凜矩;髙玉 圭樹

Nov. 2023
計測自動制御学会，システム・情報部門 学術講演会 2023
衝突危険領域と他船の衝突回避方針推定を考慮したマルチエージェント深層強化学習
［SSI研究奨励賞］, 戸板 佳祐;髙玉 圭樹

Nov. 2023
計測自動制御学会，システム・情報部門 学術講演会 2023
準最適なデモンストレーションに対応するアーカイブに基づくマルチエージェント敵対的逆強化学習
［SSI研究奨励賞］, 植木駿介;髙玉 圭樹

Oct. 2023
Arliss 2023
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2023
[Best Mission award 2nd], 髙玉研究室

Oct. 2023
Arliss 2023
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2023
[UNISEC award 1st]

Oct. 2023
Arliss 2023
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2023
[Accuracy Award 1st], 髙玉研究室

Oct. 2023
Arliss 2023
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2023
[Over All Winner], 髙玉研究室

Mar. 2023
2023 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2023)
Multi-layer Cortical Learning Algorithm for Trend Changing Time-series Forecast
[Student Paper Award Winning Papers] Multi-layer Cortical Learning Algorithm for Trend Changing Time-series Forecast, Fujino, K;Aoki, T;Takadama, K;Sato, H

Feb. 2023
計測自動制御学会
適応範囲の拡大に向けたMAMLとMLSHの組み合わせによるメタ強化学習,
[学術奨励賞 (研究奨励賞)], 加藤 駿;速水 陽平;中理 怡恒;高玉 圭樹

Dec. 2022
UNISEC (University Space Engineering Consortium) ワークショップ2022
[ポスターセッション UNISON賞3位], 髙玉研究室

Dec. 2022
進化計算学会
推定パレートフロントに基づいて重みベクトル群を配置する多目的進化アルゴリズム,
[論文賞], 高木 智章;高玉 圭樹;佐藤 寛之

Oct. 2022
計測自動制御学会、システム・情報部門 学術講演会 2022, GS04-06, pp.177-182
[優秀発表賞] 大脳新皮質学習アルゴリズムにおけるシナプスの適応配置とカラムに基づくデコーダの協調, 青木 健;高玉圭樹;佐藤寛之
Japan society

Sep. 2022
Genetic and Evolutionary Computation Conference(GECCO 2022)
Absumption based on Overgenerality and Condition-Clustering based Specialization for XCS with Continuous-Valued Inputs,
[Best Paper Awards (EML Track)], Shiraishi, H;Hayamizu, Y;Sato, H;Takadama, K

Sep. 2022
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2022,
[Technical Award 2nd prize], 髙玉研究室

Nov. 2021
計測自動制御学会，システム・情報部門 学術講演会 2021
他エージェントの不確実性にロバストな経路獲得に向けたマルチエージェント逆強化学習,
[優秀論文賞], 福本 有季子;速水 陽平;中理 怡恒;高玉 圭樹

Oct. 2021
Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.24, No.2, pp. 185--198, 2020/3/20
https://doi.org/10.20965/jaciii.2020.p0185
授賞式2021/10/7
[Best Paper Award] Self-Structured Cortical Learning Algorithm by Dynamically Adjusting Columns and Cell, Suzugamine, S;Aoki, T;Takadama, K;Sato, H

Sep. 2021
[インテリジェント・システム・シンポジウム (FAN 2021), 計測自動制御学会
ルールの過剰汎化率を考慮したAbsumption に基づく実数値学習分類子システム,
[優秀論文賞], 白石 洋輝;速水 陽平;佐藤 寛之;高玉 圭樹

Sep. 2021
インテリジェント・システム・シンポジウム (FAN 2021) , 計測自動制御学会
多目的意思決定を支援する有向パレートフロントの推定,
[プレゼンテーション賞], 高木 智章;田中 彰一郎;高玉 圭樹;佐藤 寛之

Dec. 2020
あさぎりCanSat 投下試験大会 2020
[プロフェッショナル部門賞], 髙玉研究室

Dec. 2020
あさぎりCanSat 投下試験大会 2020
[総合優勝], 髙玉研究室

Dec. 2020
あさぎりCanSat 投下試験大会 2020
[第二位受賞], 髙玉研究室

Dec. 2020
進化計算学会，第14回進化計算シンポジウム 2020
目的関数空間の単位超平面を基準とするパレートフロント推定とその利用,
[IEEE Computational Intelligence Society Japan Chapter Young Researcher Award], 高木 智章;高玉 圭樹;佐藤 寛之

Dec. 2020
進化計算学会，第14回進化計算シンポジウム 2020
学習分類子システムのルール進化に対するConditional VAE に基づく誤判定訂正,
[IEEE Computational Intelligence Society Japan Chapter Young Researcher Award], 白石 洋輝;田所 優和;速水 陽平;福本 有季子;佐藤 寛之;高玉 圭樹

Nov. 2020
計測自動制御学会、システム・情報部門 学術講演会 2020
個別探索から生成された行動系列の優先付けに基づくマルチエージェント逆強化学習
[優秀論文賞], 福本 有季子;速水 陽平;前川 佳幹;高玉 圭樹

Oct. 2020
Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII)
Approach to Clustering with Variance-Based XCS
[Young Researcher Awards 2020], Zhang, C;Tatsumi, T;Nakata, M;Takadama, K

Sep. 2020
情報処理学会，第19回情報科学技術フォーラム
モデルベース強化学習における自動計画を用いた探索戦略
[FIT論文賞], 速水 陽平;Amiri Saeid,Chandan Kishan;Zhang Shiqi;高玉 圭樹

Aug. 2020
電気学会
重みベクトルの部分集合選択による進化型多目的最適化に関する基礎検討,
[電気学会, 電子・情報・システム部門奨励賞], 高木 智章;高玉 圭樹;佐藤 寛之

Dec. 2019
UNISEC (University Space Engineering Consortium) ワークショッ プ2019
[ポスター賞第1位], 髙玉研究室
Japan society

Dec. 2019
進化計算学会, 第13回進化計算シンポジウム 2019
分解型多数目的最適化における指向確率選択と二重連鎖更新
[ベストポスター発表賞], 佐藤寛之, 髙玉圭樹
Japan society

Nov. 2019
計測自動制御学会, システム・情報部門 学術講演会 2019
多次元意見共有エージェントネットワークモデルにおける複数の環境情報発信源を考慮した誤報伝搬防止アルゴリズム
[優秀論文賞], 北島 瑛貴,村田 暁紀,上野 史,高玉 圭樹
Japan society

Sep. 2019
A Rocket Launch for International Student Satellites CanSat(ARLISS) 2019
[Technical System Award 1st prize], 髙玉研究室

Sep. 2019
A Rocket Launch for International Student Satellites CanSat(ARLISS) 2019
[Overall Winner 2nd prize], 髙玉研究室

Sep. 2019
A Rocket Launch for International Student Satellites CanSat(ARLISS) 2019
[Accuracy Award 3rd prize], 髙玉研究室

Aug. 2019
UNISEC (University Space Engineering Consortium), 能代宇宙イベント2019
[タイプエスミッション部門, 優勝], 髙玉研究室

Feb. 2019
電気学会 電子・情報・システム部門
航空機着陸問題におけるクラスタリングを用いた分割反復最適化手法
[技術委員会奨励賞], 村田 暁紀,佐藤 寛之,高玉 圭樹

Dec. 2018
UNISEC (University Space Engineering Consortium)ワークショップ2018
[ポスター賞第1位], 髙玉研究室

Dec. 2018
SCIS & ISIS
A Study on a Cortical Learning Algorithm Dynamically Adjusting Columns and Cells,
[2018 Best Paper Award], Suzugamine, S;Aoki, T;Takadama, K;Sato, H

Dec. 2018
IEEE CIS Japan Chapter, 進化計算学会，第12回進化計算シンポジウム 2018
Social Spider Optimization Algorithmの動的最適化問題への適用：変化への追従から先回りへ,
[IEEE Computational Intelligence Society Japan Chapter Young Researcher Award], 高野 諒;高玉 圭樹

Dec. 2018
UNISEC (University Space Engineering Consortium) ワークショップ2018
[ポスター賞第1位], 髙玉研究室

Oct. 2018
Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII)
[Young Researcher Awards], Matsumoto, K;Tatsumi, T;Sato, H;Kovacs, T;Takadama, K
Official journal

Sep. 2018
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2018
[UNISEC Award], 髙玉研究室
Others

Sep. 2018
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2018
[Accuracy Award 1st Place], 髙玉研究室
Others

Sep. 2018
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2018
[Technology comeback Award 2nd Place], 髙玉研究室
Others

Sep. 2018
A Rocket Launch for International Student Satellites CanSat(ARLISS) 2018
[Mission Award 3rd Place], 髙玉研究室
Others

Aug. 2018
UNISEC (University Space Engineering Consortium)
能代宇宙イベント2018
[タイプエスミッション部門，優勝], 髙玉研究室
Others

Dec. 2017
UNISEC (University Space Engineering Consortium) Workshop 2017
[ポスタセッション第1位], 髙玉研究室
Japan society

Nov. 2017
計測自動制御学会、システム・情報部門 学術講演会 2017（SSI2017), SS04-8, pp. 529--533, 2017/11/27 (表彰式)
[優秀論文賞], 辰巳 嵩豊;高玉 圭樹

Nov. 2017
計測自動制御学会、システム・情報部門 学術講演会 2017（SSI2017), GS04-8, pp. 135--140, 2017/11/27 (表彰式)
[優秀論文賞]大脳新皮質アルゴリズムの簡素化に関する検討, 青木 健;高玉 圭樹;佐藤 寛之

Aug. 2017
日本航空宇宙学会(北部支部)
特別賞学生ポスター展
[最優秀賞], 髙玉研究室
Japan society, Japan

Aug. 2017
UNISEC (University Space Engineering Consortium)
能代宇宙イベント2017
[ミッション部門能代CanSat大賞], 髙玉研究室
Others, Japan

Jun. 2017
計測自動制御学会, 第11回コンピューテーショナル・インテリジェンス研究会
多目的最適化問題における評価時間の偏りが半非同期進化法に与える影響の分析
[Young Researcher Awards], 原田 智広;高玉 圭樹

Dec. 2016
計測自動制御学会, システム・情報部門 学術講演会 2016 (SSI2016)
[学術奨励賞] 航空機到着機スケジューリングにおける最適性と多様性のトレードオフを考慮した進化計算, 村田 暁紀;佐藤 寛之;高玉 圭樹

Dec. 2016
計測自動制御学会，システム・情報部門 学術講演会 2016 (SSI2016)
[最優秀発表賞] 航空機到着機スケジューリングにおける最適性と多様性のトレードオフを考慮した進化計算, 村田 暁紀;佐藤 寛之;高玉 圭樹

Dec. 2016
計測自動制御学会, システム・情報部門 学術講演会 2016
[論文賞] Promoting Machine-code Program Evolution in Asynchronous Genetic Programming, Harada, T;Takadama, K;Sato H

Dec. 2016
UNISEC (University Space Engineering Consortium) ワークショップ2016
[口頭発表賞第1位，ポスター賞第2位], 高玉研究室
Japan society

Sep. 2016
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2016
[UNISEC Award], 高玉研究室
Others

Sep. 2016
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2016
[Accuracy Award 2nd Place], 高玉研究室
Others

Sep. 2016
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2016
[Technology Award for Ground Locomotion Mechanism], 高玉研究室
Others

Mar. 2016
計測自動制御学会 システム・情報部門 2016
[論文賞] Characteristic of Passenger's Route Selection and Generation of Public Transport Network, Majima, T;Takadama, K;Watanabe, D;Katsuhara, M

Mar. 2016
RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2016)
[Student Paper Award] A Study on Directional Repair of Infeasible Solutions for Multi-Objective Knapsack Problems, Miyakawa, M;Takadama, K;Sato, H

Dec. 2015
UNISEC (University Space Engineering Consortium) Workshop 2015
[ポスター賞第1位，団体発表賞第1位], 高玉研究室

Oct. 2015
株式会社アクセルスペース
[AXELSPACE CUP 優勝], 高玉研究室

Sep. 2015
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2015
[Best Mission Award 1st Place], 高玉研究室

Sep. 2015
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2015
[Accuracy Award 1st Place], 高玉研究室

Sep. 2015
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2015
[Technology Award Comeback Algorithms], 高玉研究室

Sep. 2015
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2015
[Technology Award Ground Locomotion Mechanism], 高玉研究室

Dec. 2014
UNISEC(University Space Engineering Consortium) Workshop 2014
[べストポスター賞], 高玉研究室

Sep. 2014
ARLISS
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2014，Precision Award, 高玉研究室

Sep. 2014
ARLISS
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2014，Coolest Award, 高玉研究室

May 2014
システム制御学会、第57回システム制御情報学会研究発表講演会 (SCI 2013)
[奨励賞] データマイニング問題を対象とした最適行動獲得のための学習分類子システムにおける個体淘汰法の検討, 中田 雅也;Pier Luca Lanzi;松島 裕康;高玉 圭樹

Dec. 2013
進化計算学会, 第7回進化計算シンポジウム 2013
[IEEE CIS Japan Chapter Young Researcher Award]指向性交配における有用な実行不可能解の選択領域制御に関する検討, 宮川 みなみ;高玉 圭樹;佐藤 寛之

Feb. 2013
計測自動制御学会, 第39回知能システムシンポジウム
[学術奨励賞（研究奨励賞)]異文化体験ゲームにおける集団適応エージェントモデルとインタラクション設計, 牛田 裕也;大谷 雅之;市川 嘉裕;佐藤 圭二;服部 聖彦;佐藤 寛之;高玉 圭樹

Feb. 2013
UNISEC Workshop 2013
[UNISEC Workshop 2013 感謝状], 高玉研究室

Feb. 2013
A Rocket Launch for International Student Satellites CanSat (ARLISS)，カムバックコンペ優勝, 高玉研究室

Dec. 2012
進化計算学会, 第6回進化計算シンポジウム 2012
[最優秀発表賞] 学習分類子システムにおける最適行動獲得のための個体選択法, 中田 雅也;Pier Luca Lanzi;松島 裕康;佐藤 寛之;高玉 圭樹

Dec. 2012
進化計算学会, 第6回進化計算シンポジウム 2012
[IEEE CIS Japan Chapter Young Researcher Award] 学習分類子システムにおける最適行動獲得のための個体選択法, 中田 雅也;Pier Luca Lanzi;松島 裕康;佐藤 寛之;高玉 圭樹

Nov. 2012
計測自動制御学会、システム・情報部門 学術講演会2012 (SSI2012)
[奨励賞]環境変化に適応するのためのスワップ型一般化, 佐藤 圭二;高玉 圭樹;大谷 雅之;市川 嘉裕;原田 智広;中田 雅也;佐藤 寛之;服部 聖彦

Sep. 2012
Triangle Symposium on Advanced ICT 2012 (TriSAI 2012)
[Student Paper Award] Interactive Assistant System for Understanding Pareto solutions in Multi-Dimensional Space, Sawadaishi, Y;Harada, T;Ichikawa, Y;Takadama, K

Feb. 2012
ARLISS
A Rocket Launch for International Student Satellites CanSat (ARLISS), 高玉研究室

Dec. 2011
進化計算学会、進化計算シンポジウム2011
[最優秀発表賞] 実行可能及び実行不可能解の並列評価による進化型多目的最適化, 島田 智大;松島 裕康;高玉 圭樹

Dec. 2011
IEEE CIS 日本支部, 第5回進化計算シンポジウム 2011
[Young Researcher Award] 実行可能及び実行不可能解の並列評価による進化型多目的最適化, 島田 智大;松島 裕康;高玉 圭樹

Sep. 2011
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2011
[カムバックコンペ第2位、第3位], 高玉研究室
United States

Dec. 2010
進化計算学会、進化計算シンポジウム2010
[ベストポスター発表賞] 個別化による学習分類子システムのマルチステップへの展開, 中田 雅也;市川 嘉裕;松島 裕康;佐藤 圭二;佐藤寛之;高玉圭樹

2010
The 61th International Astronautical Congress (IAC2010)
[国内審査会（ランバック部門）最優秀賞], 高玉研究室

Oct. 2009
The 60th International Astronautical Congress (IAC2009)
[国内審査会（ランバック部門）最優秀賞], 高玉研究室
Korea, Republic of

Sep. 2009
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2009
[Comeback Competition 1st Prize], 高玉研究室

Sep. 2009
A Rocket Launch for International Student Satellites CanSat (ARLISS) 2009
[Mission Competition 2nd Prize], 高玉研究室

Aug. 2009
JAXA
UNITEC-1(UNIsec Technological Experiment Carrier-1) Award, 高玉研究室

Aug. 2009
SICE
Certificate of Appreciation on ICCAS-SICE International Joint Conference 2009

2009
計測自動制御学会, ICCAS-SICE International Joint Conference 2009
[学術奨励賞(研究奨励賞)] Pacific Ocean Route Optimization by Pittsburgh-style Learning Classifier System, Iseya, S;Sato, K;Hattori, K;Takadama, K

Aug. 2008
SICE
Certificate of Appreciation on SICE Annual Conference 2008

2008
計測自動制御学会、第34回知能システムシンポジウム
[学術奨励賞(研究奨励賞)] 実数値学習分類子システムに関する研究, 岩崎 靖;高玉圭樹

2004
IEEE Institute of Electrical and Electronic Engineers
[Best Session Award]
United States

Jun. 2003
ISOGO Rotary Club
[感謝状], 高玉圭樹

2003
The Japanese Society of Artificial Intelligence
[ベストプレゼンテーション賞] AIと笑い - 笑いのコンテンツに含まれる構造の解析へ, 井上寛康;湯田聴夫;高玉 圭樹;下原 勝憲;片井 修

2002
Pacific Rim International Workshop on Multi-Agents (PRIMA2002)
[Summer School on Agents and Multiagent Systems ポスター賞(2件)]

2002
Advanced Telecommunications Research Institute International
[ATR Research and Development Award], 高玉圭樹

Feb. 1999
The Society of Instrument And Control Engineers
The Society of Instrument and Control Engineers Scientific Encouraging Award, 高玉圭樹

1998
The Japan Society for Management Information
[優秀論文賞], 高玉圭樹

Jun. 1997
The Japanese Society of Artificial Intelligence
[優秀論文賞], 高玉圭樹

Inverse Reinforcement Learning with Agents’ Biased Exploration Based on Sub-optimal Sequential Action Data
Fumito Uwano; Satoshi Hasegawa; Keiki Takadama
Journal of Advanced Computational Intelligence and Intelligent Informatics, 28, 2, 380-392, Mar. 2024, Peer-reviwed
Scientific journal, English

Reinforcement Learning in Cyclic Environmental Change for Non-Communicative Agents: A Theoretical Approach
Fumito Uwano; Keiki Takadama
Explainable and Transparent AI and Multi-Agent Systems (Lecture Notes in Computer Science), 14127, Sep. 2023, Peer-reviwed
International conference proceedings, English

Directional Pareto Front and Its Estimation to Encourage Multi-Objective Decision-Making
Tomoaki Takagi; Keiki Takadama; Hiroyuki Sato
IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), 11, 20619-20634, Feb. 2023, Peer-reviwed
Scientific journal

The Challenges for Socially Responsible AI for Well-being.
Takashi Kido; Keiki Takadama
AAAI Spring Symposium: SRAI, 1-3, 2023
International conference proceedings

The Challenges for Socially Responsible AI for Well-being
Takashi Kido; Keiki Takadama
CEUR Workshop Proceedings, 3527, 1-3, 2023, In this AAAI Spring Symposium 2023, we discuss Socially Responsible AI for Well-being. For AI to truly benefit society, it must go beyond mere productivity and economic advantages; embrace social responsibility; and emphasize fairness, transparency, safety, and other key principles. For instance, AI diagnostic systems should not only be accurate but also free from bias, ensuring equitable data representation across races and locations. This highlights the need for ongoing discussions on the nature of "social responsibility" in AI applications. There are two main perspectives: (1) Individually Responsible AI: Focuses on designing AI systems that consider individual well-being, such as understanding how digital experiences influence emotions and quality of life. (2) Socially Responsible AI: Emphasizes broader societal impacts, striving for decisions that are fair and beneficial for all. Addressing biases in AI is crucial to achieving fairness. Additionally, the knowledge produced by AI, such as health advice, should be universally applicable and not only beneficial to a subset of individuals. This paper outlines the underlying motivations, key terms, areas of focus, and research inquiries for this symposium.
International conference proceedings

Analyzing an Influence of Initial Bus Route Networks in Competitive Optimization of Multiple Bus Companies
Zhou, R; Yatsu, N; Nakari, I; Takadama, K
Last, SICE Journal of Control, Measurement, and System Integration (JCMSI)(to appear), 2023, Peer-reviwed

目的関数の推定類似度を用いる進化計算による多因子最適化
川上 紫央; 高玉 圭樹; 佐藤 寛之
進化計算学会論文誌, 14, 1, 40-54, 2023, Peer-reviwed

Adaptive Action-prediction Cortical Learning Algorithm under Uncertain Environments
Fujino, F; Aoki, T; Takadama K; Sato, H
International Journal of Hybrid Intelligent Systems (to appear), IOS Press, 1-21, 2023, Peer-reviwed, The cortical learning algorithm (CLA) is a time series prediction algorithm. Memory elements called columns and cells discretely represent data with their state combinations, whereas linking elements called synapses change their state combinations. For tasks requiring to take actions, the action-prediction CLA (ACLA) has an advantage to complement missing state values with their predictions. However, an increase in the number of missing state values (i) generates excess synapses negatively affect the action predictions and (ii) decreases the stability of data representation and makes the output of action values difficult. This paper proposes an adaptive ACLA using (i) adaptive synapse adjustment and (ii) adaptive action-separated decoding in an uncertain environment, missing multiple input state values probabilistically. (i) The proposed adaptive synapse adjustment suppresses unnecessary synapses. (ii) The proposed adaptive action-separated decoding adaptively outputs an action prediction separately for each action value. Experimental results using uncertain two- and three-dimensional mountain car tasks show that the proposed adaptive ACLA achieves a more robust action prediction performance than the conventional ACLA, DDPG, and the three LSTM-assisted reinforcement learning algorithms of DDPG, TD3, and SAC, even though the number of missing state values and their frequencies increase. These results implicate that the proposed adaptive ACLA is a way to making decisions for the future, even in cases where information surrounding the situation partially lacked.
Scientific journal

Multi-layered Cortical Learning Algorithm for Forecasting Time-series Data with Probabilistically Changing Trends
Fujino, F; Aoki, T; Takadama K; Sato, H
Journal of Signal Processing (to appear), 2023, Peer-reviwed

マルコフ連鎖に基づく局所解ネットワークと(1+1)-EAの性能予測
田中 彰一郎; 古谷 博史; 日和 悟; 廣安 知之; 高玉 圭樹; 佐藤 寛之
進化計算学会論文誌, 13, 1, 40-52, Oct. 2022, Peer-reviwed
Scientific journal, Japanese

Benefit Balancing between Japan Departure Flights and Overflights in the North Pacific Route System.
Murata, A; Takadama, K; Toratani, D; Hirabayashi, H; Brown, M
Transactions of the Japan Society for Aeronautical and Space Sciences, Areaspace technology Japan, 20, 41-48, Aug. 2022, Peer-reviwed
Scientific journal, English

Design of Human-Agent-Group Interaction for Correct Opinion Sharing on Social Media
Fumito Uwano; Daiki Yamane; Keiki Takadama
Proceedings of 24th International Conference on Human-Computer Interaction (HCII 2022), 26 Jun. 2022, Peer-reviwed, Invited
International conference proceedings, English

実ロボット適用に向けた複数局所解探索のための複数群間移動に基づく群知能最適化
前川 裕介; 河野 航大; 梶原 奨; 福本 有季子; 佐藤 寛之; 高玉 圭樹
Last, 進化計算学会論文誌, 12, 3, 125-136, Feb. 2022, Peer-reviwed
Scientific journal, Japanese

航空機着陸問題における混雑時に対応するクラスタリングを用いた分割反復最適化手法
村田 暁紀; 佐藤 寛之; 高玉 圭樹; デライエ ダニエル
Corresponding, 電気学会論文誌Ｃ(電子・情報・システム部門誌), 142, 2, 198-205, 01 Feb. 2022, Peer-reviwed
Scientific journal, Japanese

The Challenges for Fairness and Well-being.
Takashi Kido; Keiki Takadama
AAAI Spring Symposium: HFIF, 1-3, 2022
International conference proceedings

The Challenges for Fairness and Well-being - How Fair is Fair? Achieving Well-being AI - How F
Takashi Kido; Keiki Takadama
CEUR Workshop Proceedings, 3276, 1-3, 2022, In the AAAI Spring Symposium 2022, we discussed fairness and well-being in the context of well-being AI. One of the important keywords is "well-being."We define "well-being AI"as Artificial Intelligence that promotes psychological well-being (i.e., happiness) and maximizes human potential ability. The well-being AI helps understand how our digital experience affects our emotions and quality of life and how to design a better well-being system that puts humans at the center. The second important keyword is "fairness."AI can potentially assist humans in making fair decisions. However, we must tackle the "bias"problem in AI (and in humans) to achieve fairness. Although statistical machine learning predicts the future based on past data, several types of data biases may lead to an AI-based system making incorrect predictions. For AI to be deployed safely, these systems must be wellunderstood, and we need to understand "How fair is fair"for achieving "Well-being AI."This paper describes the motivation, scope of interest, and research questions of this symposium.
International conference proceedings

Formalizing Model-Based Multi-Objective Reinforcement Learning With a Reward Occurrence Probability Vector
Tomohiro Yamaguchi; Yuto Kawabuchi; Shota Takahashi; Yoshihiro Ichikawa; Keiki Takadama
Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning, IGI Global, 299-330, 2022, The mission of this chapter is to formalize multi-objective reinforcement learning (MORL) problems where there are multiple conflicting objectives with unknown weights. The objective is to collect all Pareto optimal policies in order to adapt them for use in a learner's situation. However, it takes huge learning costs in previous methods, so this chapter proposes the novel model-based MORL method by reward occurrence probability (ROP) with unknown weights. There are three main features. First one is that average reward of a policy is defined by inner product of the ROP vector and a weight vector. Second feature is that it learns the ROP vector in each policy instead of Q-values. Third feature is that Pareto optimal deterministic policies directly form the vertices of a convex hull in the ROP vector space. Therefore, Pareto optimal policies are calculated independently with weights and just one time by Quickhull algorithm. This chapter reports the authors' current work under the stochastic learning environment with up to 12 states, three actions, and three and four reward rules.
In book

推定パレートフロントに基づいて重みベクトル群を配置する多目的進化アルゴリズム
高木 智章; 高玉 圭樹; 佐藤 寛之
進化計算学会論文誌, 12, 2, 45-60, Jan. 2022, Peer-reviwed
Scientific journal, Japanese

時間的・空間的観点に基づく粒子群最適化と差分進化の個体別選択
河野 航大; 梶原 奨; 高野 諒; 佐藤 寛之; 高玉 圭樹
Last, 進化計算学会論文誌, 12, 1, 1-11, Dec. 2021, Peer-reviwed
Scientific journal, Japanese

複雑ネットワークに基づく多次元意見共有モデル上の誤報伝搬防止
上野 史; 北島 瑛貴; 高玉 圭樹
Last, 人工知能学会論文誌, 36, 6, B-KB2_1-12, 01 Nov. 2021, Peer-reviwed
Scientific journal, Japanese

Adaptive Synapse Arrangement in Cortical Learning Algorithm
Aoki, T; Takadama, K; Sato, H
Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), 2021, 25, 4, 450-466, 21 Jul. 2021, Peer-reviwed
Scientific journal, English

Analyzing Early Stage of Forming a Consensus from Viewpoint of Majority/Minority Decision in Online-Barnga
Yoshimiki Maekawa; Tomohiro Yamaguchi; Keiki Takadama
Lecture Notes in Computer Science, Springer International Publishing, 269-285, 03 Jul. 2021
In book

評価値軸・設計変数上の解の継続変化に対する群知能アルゴリズのためのメカニズムの設計とその追従性の評価
高野 諒; 佐藤 寛之; 高玉 圭樹
Last, 進化計算学会論文誌, 11, 3, 29-44, Mar. 2021, Peer-reviwed
Scientific journal, Japanese

Towards Agent Design for Forming a Consensus Remotely Through an Analysis of Declaration of Intent in Barnga Game
Yoshimiki Maekawa; Tomohiro Yamaguchi; Keiki Takadama
Advances in Intelligent Systems and Computing, Springer International Publishing, 540-546, 26 Jan. 2021
In book

Multi-factorial Evolutionary Algorithm Using Objective Similarity Based Parent Selection
Shio Kawakami; Keiki Takadama; Hiroyuki Sato
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer International Publishing, 45-60, 2021
In book

Multi-value opinion sharing based on information source influence in agent-based network
Kitajima, E; Murata, A; Takadama, K
Last, Journal of Physics: Conference Series, IGI Global, 1564, 29 Jun. 2020, Peer-reviwed
Scientific journal, English

Reward Value-Based Goal Selection for Agents’ Cooperative Route Learning Without Communication in Reward and Goal Dynamism
Fumito Uwano; Keiki Takadama
Last, SN Computer Science, Springer Science and Business Media LLC, 1, 3, May 2020, Peer-reviwed
Scientific journal

Self-Structured Cortical Learning Algorithm by Dynamically Adjusting Columns and Cell
Suzugamine, S; Aoki, T; Takadama, K; Sato, H
Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), 24, 2, 185-198, 20 Mar. 2020, Peer-reviwed
Scientific journal, English

Model-Based Multi-Objective Reinforcement Learning by a Reward Occurrence Probability Vector
Tomohiro Yamaguchi; Shota Nagahama; Yoshihiro Ichikawa; Yoshimichi Honma; Keiki Takadama
Advanced Robotics and Intelligent Automation in Manufacturing, IGI Global, 269-295, 2020, This chapter describes solving multi-objective reinforcement learning (MORL) problems where there are multiple conflicting objectives with unknown weights. Previous model-free MORL methods take large number of calculations to collect a Pareto optimal set for each V/Q-value vector. In contrast, model-based MORL can reduce such a calculation cost than model-free MORLs. However, previous model-based MORL method is for only deterministic environments. To solve them, this chapter proposes a novel model-based MORL method by a reward occurrence probability (ROP) vector with unknown weights. The experimental results are reported under the stochastic learning environments with up to 10 states, 3 actions, and 3 reward rules. The experimental results show that the proposed method collects all Pareto optimal policies, and it took about 214 seconds (10 states, 3 actions, 3 rewards) for total learning time. In future research directions, the ways to speed up methods and how to use non-optimal policies are discussed.
In book

目的制限に基づく通信なしマルチエージェント協調行動学習とその効果の証明
上野 史; 高玉 圭樹
Last, 電気学会論文誌C, Vol. 140, No. 1, 75-84, Jan. 2020, Peer-reviwed
Scientific journal, Japanese

Utilizing Observed Information for No-Communication Multi-agent Reinforcement Learning toward Cooperation in Dynamic Environment
Uwano, F; Takadama, K
Last, SICE Journal of Control, Measurement, and System Integration (JCMSI), Vol. 12, No. 5, 199-208, 30 Sep. 2019, Peer-reviwed
Scientific journal, English

How to Design Adaptable Agents to Obtain a Consensus with Omoiyari.
Yoshimiki Maekawa; Fumito Uwano; Eiki Kitajima; Keiki Takadama
Human Interface and the Management of Information. Visual Information and Knowledge Management - Thematic Area, HIMI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26-31, 2019, Proceedings, Part I, Springer, 462-475, Jul. 2019, Peer-reviwed

Model-Based Multi-objective Reinforcement Learning with Unknown Weights
Tomohiro Yamaguchi; Shota Nagahama; Yoshihiro Ichikawa; Keiki Takadama
Human Interface and the Management of Information. Information in Intelligent Systems, Springer International Publishing, 311-321, 29 Jun. 2019
In book

Niche Radius Adaptation in Bat Algorithm for Locating Multiple Optima in Multimodal Functions
Takuya Iwase; Ryo Takano; Fumito Uwano; Hiroyuki Sato; Keiki Takadama
2019 IEEE Congress on Evolutionary Computation (CEC), IEEE, Jun. 2019
International conference proceedings

Acquiring Classifiers for Bipolarized Reward by XCS in a Continuous Reward Environment
Tatsumi, T; Takadama, K
Last, SICE Journal of Control, Measurement, and System Integration (JCMSI), Vol. 12, No. 3, 124-132, 31 May 2019, Peer-reviwed
Scientific journal, English

Analysis of semi-asynchronous multi-objective evolutionary algorithm with different asynchronies
Harada, T; Takadama, K
Last, Soft Computing, 1-23, 20 May 2019, Peer-reviwed
Scientific journal, English

Artificial Bee Colony Algorithm based on Adaptive Local Information Sharing meets multiple dynamic environments
Takano, R; Sato, H; Takadama, K
Last, SICE Journal of Control, Measurement, and System Integration (JCMSI), 12, 1, 1-10, 19 Jan. 2019, Peer-reviwed
Scientific journal, English

The Challenges for Interpretable AI for Well-being.
Takashi Kido; Keiki Takadama
Proceedings of the Symposium Interpretable AI for Well-being: Understanding Cognitive Bias and Social Embeddedness co-located with Association for the Advancement of Artificial Intelligence 2019 Spring Symposium (AAAI-Spring Symposium 2019), CEUR-WS.org, 2019
International conference proceedings

The challenges for interpretable AI for well-being -understanding cognitive bias and social embeddedness-
Takashi Kido; Keiki Takadama
CEUR Workshop Proceedings, 2448, 2019, In this AAAI Spring symposium 2019, we discuss interpretable AI in the context of well-being AI. Interpretable AI is an artificial intelligence methods and systems, of which outputs can be easily understood by humans. Especially in the human health and wellness domains, making wrong predictions may lead to critical judgements in life or death situations. AI based systems must be well-understood. We define “well-being AI” as an AI research paradigm for promoting psychological well-being and maximizing human potential. Interpretable AI is important for well-being AI in senses that (1) to understand how our digital experience affects our health and our quality of life and (2) to design well-being systems that put humans at the center. One of the important keywords in understanding machine intelligence in human health and wellness is cognitive bias. Advances in big data and machine learning should not overlook some new threats to enlightened thought, such as the recent trend of social media platforms and commercial recommendation systems being used to manipulate people's inherent cognitive bias. The second important keyword is “social embeddedness”. Cognitive bias will be affected by how the AI is perceived particularly at the community or social level. Social embeddedness is the social science idea that actions of individuals are refracted by the social relations within their community. In our contexts, understanding relationships between AI and society is very important, which includes the issues on AI and future economics (such as basic income, impact of AI on GDP), or “well-being society (such as happiness of citizen life quality). This paper describes the detailed motivation, important keywords, the scope of interests and research questions in this symposium.
International conference proceedings

Awareness Based Recommendation
Tomohiro Yamaguchi; Takuma Nishimura; Keiki Takadama
Human Performance Technology, IGI Global, 167-186, 2019, In Artificial Intelligence and Robotics, one of the important issues is to design Human interface. There are two issues, one is the machine-centered interaction design to adapt humans for operating the robots or systems. Another one is the human-centered interaction design to make it adaptable for humans. This research aims at latter issue. This paper presents the interactive learning system to assist positive change in the preference of a human toward the true preference, then evaluation of the awareness effect is discussed. The system behaves passively to reflect the human intelligence by visualizing the traces of his/her behaviors. Experimental results showed that subjects are divided into two groups, heavy users and light users, and that there are different effects between them under the same visualizing condition. They also showed that the authors' system improves the efficiency for deciding the most preferred plan for both heavy users and light users.
In book

Awareness-Based Recommendation Toward a New Preference
Tomohiro Yamaguchi; Takuma Nishimura; Keiki Takadama
Human Performance Technology, IGI Global, 572-593, 2019, In mechatronics and robotics, one of the important issues is to design human interface. There are two issues on interaction design research. One is the way to education and training to adapt humans for operating the robots or interaction systems. Another one is the way to make interaction design adaptable for humans. This chapter research at the latter issue. This chapter describes the interactive learning system to assist positive change in the preference of a human toward the true preference; then evaluation of the awareness effect is discussed. The system behaves passively to reflect the human intelligence by visualizing the traces of his/her behaviors. Experimental results showed that subjects are divided into two groups, heavy users and light users, and that there are different effects between them under the same visualizing condition. They also showed that the system improves the efficiency for deciding the most preferred plan for both heavy users and light users.
In book

Transportation Simulator for Disaster Circumstance and Bottleneck Analysis
Majima, T; Takadama, K; Watanabe, D; Aratani, T; Sato, K
Corresponding, Journal Artificial Life and Robotics, Springer--Verlag, 23, 4, 593-599, 03 Oct. 2018, Peer-reviwed
Scientific journal, English

Strategy for Learning Cooperative Behavior with Local Information for Multi-agent Systems
Fumito Uwano; Keiki Takadama
Proceedings of The 21st International Conference on Principles and Practice of Multi-Agent Systems, 663-670, Oct. 2018, Peer-reviwed
International conference proceedings, English

Exploring Tradeoff Between Distance-minimality and Diversity of Landing Routes for Aircraft Landing Optimization
Murata, A; Sato, H; Takadama, K
Last, SICE Journal of Control, Measurement, and System Integration (JCMSI), 11, 5, 409-418, 30 Sep. 2018, Peer-reviwed
Scientific journal, English

Multi-Agent Cooperation Based on Reinforcement Learning with Internal Reward in Maze Problem
Uwano, F; Tatebe, N; Nakata, M; Tajima, Y; Kovacs, T; Takadama, K
Last, SICE Journal of Control, Measurement, and System Integration (JCMSI), SICE Journal of Control, Measurement, and System Integration (JCMSI), Vol. 11, No. 4, 321-330, 31 Jul. 2018, Peer-reviwed
Scientific journal, English

Weighted Opinion Sharing Model for Cutting Link and Changing Information among Agents as Dynamic Environmen
Uwano, F; Saito, R; Takadama, K
Last, SICE Journal of Control, Measurement, and System Integration (JCMSI), Vol. 11, No. 4, 331-340, 31 Jul. 2018, Peer-reviwed
Scientific journal, English

Correcting Wrongly Determined Opinions of Agents in Opinion Sharing Model.
Eiki Kitajima; Caili Zhang; Haruyuki Ishii; Fumito Uwano; Keiki Takadama
Human Interface and the Management of Information. Interaction, Visualization, and Analytics - 20th International Conference, HIMI 2018, Held as Part of HCI International 2018, Las Vegas, NV, USA, July 15-20, 2018, Proceedings, Part I, Springer, 658-676, Jul. 2018, Peer-reviwed

Generalizing rules by random forest-based learning classifier systems for high-dimensional data mining.
Fumito Uwano; Koji Dobashi; Keiki Takadama; Tim Kovacs
Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, 1465-1472, Jul. 2018, Peer-reviwed

Multiple swarm intelligence methods based on multiple population with sharing best solution for drastic environmental change.
Yuta Umenai; Fumito Uwano; Hiroyuki Sato; Keiki Takadama
Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, 97-98, Jul. 2018, Peer-reviwed

Generating Learning Environments Derived from Found Solutions by Adding Sub-goals Toward the Creative Learning Support
Takato Okudo; Tomohiro Yamaguchi; Keiki Takadama
Human Interface and the Management of Information. Information in Applications and Services, Springer International Publishing, 313-330, 07 Jun. 2018
In book

An Empirical Analysis of Action Map in Learning Classifier Systems
Nakata, M; Takadama K
Last, SICE Journal of Control, Measurement, and System Integration (JCMSI), 11, 3, 239-248, 31 May 2018, Peer-reviwed
Scientific journal, English

相似な三角形に基づくクレータマッチングによるSLIM探査機の自己位置推定とその精度向上
石井 晴之; 村田 暁紀; 上野 史; 辰巳 嵩豊; 梅内 祐太; 高玉 圭樹; 原田 智広; 鎌田 弘之; 石田 貴行; 福田 盛介; 澤井 秀次郎; 坂井 真一郎
日本航空宇宙学会, 航空宇宙技術, 日本航空宇宙学会 JSASS-D-17-00011, Vol. 17, 69-78, 24 Mar. 2018, Peer-reviwed
Scientific journal, Japanese

Towards Adaptive Aircraft Landing Order with Aircraft Routes Partially Fixed by Air Traffic controllers as Human Intervention
Murata, A; Sato, H; Takadama, K
Last, SICE Journal of Control, Measurement, and System Integration (JCMSI), vol.11, No.2, 105-112, 24 Mar. 2018, Peer-reviwed
Scientific journal, English

Sleep Stage Estimation Comparing own past heartrate or other's heartrate
Tajima, Y; Uwano, F; Murata, A; Harada, T; Takadama, K
Last, SICE Journal of Control, Measurement, and System Integration (JCMSI), Vol.11, No.1, 32-39, 06 Mar. 2018, Peer-reviwed
Scientific journal, English

Analyzing the Goal Finding Process of Human's Continuous Learning with the Reflection Subtask
Yamaguchi, T; Tamai, Y; Honma, Y; Takadama, K
Last, SICE Journal of Control, Measurement, and System Integration (JCMSI), Vol.11, No.1, 40-47, 06 Mar. 2018, Peer-reviwed
Scientific journal, English

主成分分析によるクレータ座標・サイズの検出とその評価
岡田 怜史; 中浜 優佳; 森部 美沙子; 鎌田 弘之; 狩谷 和季; 高玉 圭樹; 石田 貴行; 福田 盛介; 澤井 秀次郎; 坂井 真一郎
日本航空宇宙学会, 航空宇宙技術, Vol. 17, 61-67, 06 Mar. 2018, Peer-reviwed
Scientific journal, Japanese

Ensemble Heart Rate Extraction Method for Biological Data from Water Pressure Sensor.
Fumito Uwano; Keiki Takadama
2018 AAAI Spring Symposia, Stanford University, Palo Alto, California, USA, March 26-28, 2018., AAAI Press, 2018, Peer-reviwed

Improving Sleep Stage Estimation Accuracy by Circadian Rhythm Extracted from a Low Frequency Component of Heart Rate.
Akari Tobaru; Fumito Uwano; Takuya Iwase; Kazuma Matsumoto; Ryo Takano; Yusuke Tajima; Yuta Umenai; Keiki Takadama
2018 AAAI Spring Symposia, Stanford University, Palo Alto, California, USA, March 26-28, 2018., AAAI Press, 2018, Peer-reviwed

The Challenges for Understanding Cognitive Bias and Humanity for Well-Being AI - Beyond Machine Intelligence.
Takashi Kido; Keiki Takadama
2018 AAAI Spring Symposia, AAAI Press, 2018
International conference proceedings

The challenges for understanding cognitive bias and humanity for well-being AI — Beyond machine intelligence
Takashi Kido; Keiki Takadama
AAAI Spring Symposium - Technical Report, 2018-March, 237-238, 2018, In this AAAI Spring symposium 2018, we discuss cognitive bias and humanity in the context of well-being AI. We define “well-being AI” as an AI research paradigm for promoting psychological well-being and maximizing human potential. The goals of well-being AI are (1) to understand how our digital experience affects our health and our quality of life and (2) to design well-being systems that put humans at the center. The important challenges of this research are how to quantify subjective things such as happiness, personal impressions, and personal values, and how to transform them into scientific representations with corresponding computational methods. One of the important keywords in understanding machine intelligence in human health and wellness is cognitive bias. Advances in big data and machine learning should not overlook some new threats to enlightened thought, such as the recent trend of social media platforms and commercial recommendation systems being used to manipulate people's inherent cognitive bias. The second important keyword is humanity. Rational thinking, on which early AI researchers had been focused their efforts, is recently and rapidly replacing human thinking by machines. Many people might have begun to believe that irrational thinking is the root of humanity. Empirical and philosophical discussions on AI and humanity would be welcome. This paper describes the detailed motivation, technical, and philosophical challenges of this symposium proposal.
International conference proceedings

Analyzing the Goal-Finding Process of Human Learning With the Reflection Subtask
Tomohiro Yamaguchi; Yuki Tamai; Keiki Takadama
Handbook of Research on Biomimetics and Biomedical Robotics, IGI Global, 442-459, 2018, This chapter reports the authors' experimental results on analyzing the human goal-finding process in continuous learning. The objective of this research is to make clear the mechanism of continuous learning. To fill in the missing piece of reinforcement learning framework for the learning robot, the authors focus on two human mental learning processes, awareness as pre-learning process and reflection as post-learning process. To observe mental learning processes of a human, the authors propose a new method for visualizing them by the reflection subtask for human to be aware of the goal-finding process in continuous learning with invisible mazes. The two-layered task is introduced. The first layer is the main task of continuous learning designing the environmental mastery task to accomplish the goal for any environment. The second layer is the reflection subtask to make clear the goal-finding process in continuous learning. The reflection cost is evaluated to analyze it.
In book

Designing the Learning Goal Space for Human Toward Acquiring a Creative Learning Skill
Takato Okudo; Tomohiro Yamaguchi; Keiki Takadama
Handbook of Research on Biomimetics and Biomedical Robotics, IGI Global, 460-475, 2018, This chapter presents the way to design a learning support system toward acquiring a creative skill on learning. There are two research goals. One is to establish designing the creative learning task. The other is to make clear the human sense of creativity. As the background of this research, the jobs with high creativity or social skills will remain in the future. However, acquiring human's creativity is too difficult for computers. To solve this problem, the authors focus on the way to utilize higher creativity of human than that of computers. The main method is the visualization of learning traces to support awareness for creativity on the learning. The authors conducted the preliminary learning experiment with three human subjects. After that, the questionnaire and the hearing investigation were conducted. As the future work, the authors are planning to conduct an updated version of the experiment.
In book

Machine-code program evolution by genetic programming using asynchronous reference-based evaluation through single-event upset in on-board computer
Tomohiro Harada; Keiki Takadama
Last, Journal of Robotics and Mechatronics, Fuji Technology Press, 29, 5, 808-818, 01 Oct. 2017, Peer-reviwed, This study proposes a novel genetic programming method using asynchronous reference-based evaluation (called AREGP) to evolve computer programs through single-event upsets (SEUs) in the on-board computer in space missions. AREGP is an extension of Tierra-based asynchronous genetic programming (TAGP), which was proposed in our previous study. It is based on the idea of the biological simulator, Tierra, where digital creatures are evolved through bit inversions in a program. AREGP not only inherits the advantages of TAGP but also overcomes its limitation, i.e., TAGP cannot select good programs for evolution without an appropriate threshold. Specifically, AREGP introduces an archive mechanism to maintain good programs and a reference-based evaluation by using the archive for appropriate threshold selection and removal. To investigate the effectiveness of the proposed AREGP, simulation experiments are performed to evolve the assembly language program in the SEU environment. In these experiments, the PIC instruction set, which is carried on many types of spacecraft, is used as the evolved assembly program. The experimental results revealed that AREGP cannot only maintain the correct program through SEU with high occurrence rate, but is also better at reducing the size of programs in comparison with TAGP. Additionally, AREGP can achieve a shorter execution step and smaller size of programs, which cannot be achieved by TAGP.
Scientific journal, English

Recovery system based on exploration-biased genetic algorithm for stuck rover in planetary exploration
Fumito Uwano; Yusuke Tajima; Akinori Murata; Keiki Takadama
Last, Journal of Robotics and Mechatronics, Fuji Technology Press, 29, 5, 877-886, 01 Oct. 2017, Peer-reviwed, Contributing toward continuous planetary surface exploration by a rover (i.e., a space probe), this paper proposes (1) an adaptive learning mechanism as the software system, based on an exploration-biased genetic algorithm (EGA), which intends to explore several behaviors, and (2) a recovery system as the hardware system, which helps a rover exit stuck areas, a kind of immobilized situation, by testing the explored behaviors. We develop a rover-type space probe, which has a stabilizer with two movable joints like an arm, and learns how to use them by employing EGA. To evaluate the effectiveness of the recovery system based on the EGA, the following two field experiments are conducted with the proposed rover: (i) a small field test, including a stuck area created artificially in a park
and (ii) a large field test, including several stuck areas in Black Rock Desert, USA, as an analog experiment for planetary exploration. The experimental results reveal the following implications: (1) the recovery system based on the EGA enables our rover to exit stuck areas by an appropriate sequence of motions of the two movable joints
and (2) the success rate of getting out of stuck areas is 95% during planetary exploration.
Scientific journal, English

XCSR learning from compressed data acquired by deep neural network
Kazuma Matsumoto; Takato Tatsumi; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
Last, Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, 21, 5, 856-867, 01 Sep. 2017, Peer-reviwed, The correctness rate of classification of neural networks is improved by deep learning, which is machine learning of neural networks, and its accuracy is higher than the human brain in some fields. This paper proposes the hybrid system of the neural network and the Learning Classifier System (LCS). LCS is evolutionary rule-based machine learning using reinforcement learning. To increase the correctness rate of classification, we combine the neural network and the LCS. This paper conducted benchmark experiments to verify the proposed system. The experiment revealed that: 1) the correctness rate of classification of the proposed system is higher than the conventional LCS (XCSR) and normal neural network
and 2) the covering mechanism of XCSR raises the correctness rate of proposed system.
Scientific journal, English

Exemplar-based learning classifier system with dynamic matching range for imbalanced data
Hiroyasu Matsushima; Keiki Takadama
Last, Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, 21, 5, 868-875, 01 Sep. 2017, Peer-reviwed, In this paper, we propose a method to improve ECSDMR which enables appropriate output for imbalanced data sets. In order to control generalization of LCS in imbalanced data set, we propose a method of applying imbalance ratio of data set to a sigmoid function, and then, appropriately update the matching range. In comparison with our previous work (ECSDMR), the proposed method can control the generalization of the appropriate matching range automatically to extract the exemplars that cover the given problem space, wchich consists of imbalanced data set. From the experimental results, it is suggested that the proposed method provides stable performance to imbalanced data set. The effect of the proposed method using the sigmoid function considering the data balance is shown.
Scientific journal, English

Approach to clustering with variance-based XCS
Caili Zhang; Takato Tatsumi; Masaya Nakata; Keiki Takadama
Last, Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, 21, 5, 885-894, 01 Sep. 2017, Peer-reviwed, This paper presents an approach to clustering that extends the variance-based Learning Classifier System (XCS-VR). In real world problems, the ability to combine similar rules is crucial in the knowledge discovery and data mining field. Conventionally, XCS-VR is able to acquire generalized rules, but it cannot further acquire more generalized rules from these rules. The proposed approach (called XCS-VRc) accomplishes this by integrating similar generalized rules. To validate the proposed approach, we designed a benchmark problem to examine whether XCS-VRc can cluster both the generalized andmore generalized features in the input data. The proposed XCS-VRc proved to be more efficient than XCS and the conventional XCSVR.
Scientific journal, English

Learning classifier system based on mean of reward
Takato Tatsumi; Hiroyuki Sato; Keiki Takadama
Last, Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, 21, 5, 895-906, 01 Sep. 2017, Peer-reviwed, This paper focuses on the generalization of classifiers in noisy problems and aims at construction learning classifier system (LCS) that can acquire the optimal classifier subset by dynamically determining the classifier generalization criteria. In this paper, an accuracy-based LCS (XCS) that uses the mean of the reward (XCS-MR) is introduced, which can correctly identify classifiers as either accurate or inaccurate for noisy problems, and investigates its effectiveness when used for several noisy problems. Applying XCS and an XCS based on the variance of reward (XCS-VR) as the conventional LCSs, along with XCS-MR, to noisy 11-multiplexer problems where the reward value changes according to a Gaussian distribution, Cauchy distribution, and lognormal distribution revealed the following: (1) XCS-VR and XCS-MR could select the correct action for every type of reward distribution
(2) XCSMR could appropriately generalize the classifiers with the smallest amount of data
and (3) XCS-MR could acquire the optimal classifier subset in every trial for every type of reward distribution.
Scientific journal, English

Supporting the exploration of the learning goals for a continuous learner toward creative learning
Takato Okudo; Tomohiro Yamaguchi; Akinori Murata; Takato Tatsumi; Fumito Uwano; Keiki Takadama
Last, Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, 21, 5, 907-916, 01 Sep. 2017, Peer-reviwed, This paper proposes a learning goal space that visualizes the distribution of the obtained solutions to support the exploration of the learning goals for a learner. Subsequently, we examine the method for assisting a learner to present the novelty of the obtained solution. We conduct a learning experiment using a continuous learning task to identify various solutions. To assign the subjects space to explore the learning goals, several parameters related to the success of the task are not instructed to the subjects. In the comparative experiment, three types of learning feedbacks provided to the subjects are compared. These are presenting the learning goal space with obtained solutions mapped on it, directly presenting the novelty of the obtained solutions mapped on it, and presenting some value that is slightly related to the obtained solution. In the experiments, the subjects to whom the learning goal space or novelty of the obtained solution is shown, continue to identify solutions according to their learning goals until the final stage in the experiment is attained. Therefore, in a continuous learning task, our supporting method of directly or indirectly presenting the novelty of the obtained solution through the learning goal space is effective.
Scientific journal, English

Comparison between reinforcement learning methods with different goal selections in multi-agent cooperation
Fumito Uwano; Keiki Takadama
Last, Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, 21, 5, 917-929, 01 Sep. 2017, Peer-reviwed, This study discusses important factors for zero communication, multi-agent cooperation by comparing different modified reinforcement learning methods. The two learning methods used for comparison were assigned different goal selections for multi-agent cooperation tasks. The first method is called Profit Minimizing Reinforcement Learning (PMRL)
it forces agents to learn how to reach the farthest goal, and then the agent closest to the goal is directed to the goal. The second method is called Yielding Action Reinforcement Learning (YARL)
it forces agents to learn through a Q-learning process, and if the agents have a conflict, the agent that is closest to the goal learns to reach the next closest goal. To compare the two methods, we designed experiments by adjusting the following maze factors: (1) the location of the start point and goal
(2) the number of agents
and (3) the size of maze. The intensive simulations performed on the maze problem for the agent cooperation task revealed that the two methods successfully enabled the agents to exhibit cooperative behavior, even if the size of the maze and the number of agents change. The PMRL mechanism always enables the agents to learn cooperative behavior, whereas the YARL mechanism makes the agents learn cooperative behavior over a small number of learning iterations. In zero communication, multi-agent cooperation, it is important that only agents that have a conflict cooperate with each other.
Scientific journal, English

Theoretical XCS parameter settings of learning accurate classifiers.
Masaya Nakata; Will N. Browne; Tomoki Hamagami; Keiki Takadama
Last, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, Berlin, Germany, July 15-19, 2017, ACM, 473-480, 17 Jul. 2017, Peer-reviwed

A study of self-Adaptive semi-Asynchronous evolutionary algorithm on multi-objective optimization problem
Tomohiro Harada; Keiki Takadama
Last, Tthe Genetic and Evolutionary Computation Conference Companion (GECCO 2017), Association for Computing Machinery, Inc, 1812-1819, 15 Jul. 2017, Peer-reviwed, This paper proposes a self-Adaptive semi-Asynchronous evolutionary algorithm, SA2EA for short, and verifies its effectiveness on multi-objective optimization problems. SA2EA is an extension of an asynchronous EA that continuously evolves solutions whenever one solution completes its evaluation in a parallel computation environment, unlike a conventional generation-based synchronous EA needs to wait for evaluations of all solutions in a population, which causes to waste much idle time of parallel computation nodes. In contrast to such asynchronous EA, SA2EA adequately controls its asynchrony, which means the number of waited solutions, depends on the variance of evaluation time of solutions. To investigate the effectiveness of the proposed SA2EA, this paper conducts the experiment on benchmark problems of multi-objective optimization where several variations of the variance of evaluation time are tested in pseudo-parallel computation environment. The experimental result reveals that the proposed SA2EA outperforms the synchronous and the asynchronous EA with constant asynchrony not depends on the variance of evaluation time of solutions.
International conference proceedings, English

Performance comparison of parallel asynchronous multi-objective evolutionary algorithm with different asynchrony
Tomohiro Harada; Keiki Takadama
Last, 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 1215-1222, 05 Jul. 2017, Peer-reviwed, This paper proposes a parallel asynchronous evolutionary algorithm (EA) with different asynchrony and verifies its effectiveness on multi-objective optimization problems. We represent such EA with different asynchrony as semi-asynchronous EA. The semi-asynchronous EA continuously evolves solutions whenever a part of solutions in the population completes their evaluations in the master-slave parallel computation environment, unlike a conventional synchronous EA, which waits for evaluations of all solutions to generate next population. To establish the semi-asynchronous EA, this paper proposes the asynchrony parameter to decide how many solutions are waited, and clarifies the effectual asynchrony related to the number of slave nodes. In the experiment, we apply the semi-asynchronous EA to NSGA-II, which is a well-known multi-objective evolutionary algorithm, and the semi-asynchronous NSGA-IIs with different asynchrony are compared with synchronous one on the multi-objective optimization benchmark problems with several variances of evaluation time. The experimental result reveals that the semi-asynchronous NSGA-II with low asynchrony has possibility to perform the best search ability than the complete asynchronous and the synchronous NSGA-II in the optimization problems with large variance of evaluation time.
International conference proceedings, English

An Improved MOEA/D Utilizing Variation Angles for Multi-objective Optimization
Hiroyuki Sato; Minami Miyakawa; Keiki Takadama
Proc. of 2017 Genetic and Evolutionary Computation Conference (GECCO 2017), 163-164, Jul. 2017, Peer-reviwed

SIMULATION MODEL FOR EMERGENCY MEDICAL TRANSPORTATION WITH DEPLOYMENT OF FLOATING MEDICAL SUPPORT SYSTEM-THE CASE STUDY OF TOKYO INLAND EARTHQUAKE -
Daisuke Watanabe; Takahiro Majima; Keiki Takadama
Last, Proceedings of International Scheduling Symposium 2017 (ISS 2017), Nagoya, Jun. 2017, Peer-reviwed

Strategies to Improve Cuckoo Search Toward Adapting Randomly Changing Environment.
Yuta Umenai; Fumito Uwano; Hiroyuki Sato; Keiki Takadama
Last, Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Fukuoka, Japan, July 27 - August 1, 2017, Proceedings, Part I, Springer, 573-582, Jun. 2017, Peer-reviwed

The robust spacecraft location estimation algorithm toward the misdetection crater and the undetected crater in SLIM
Haruyuki Ishii; Keiki Takadama; Akinori Murata; Fumito Uwano; Takato Tatsumi; Yuta Umenai; Kazuma Matsumoto; Hiroyuki Kamata; Takayuki Ishida; Seisuke Fukuda; Shujiro Sawai; Shinichiro Sakai
Proceedings of International Symposium on Space Technology and Science, ISTS 2017, Jun. 2017, Peer-reviwed
International conference proceedings, English

Designing the Learning Goal Space for Human Toward Acquiring a Creative Learning Skill
Takato Okudo; Keiki Takadama; Tomohiro Yamaguchi
Human Interface and the Management of Information: Supporting Learning, Decision-Making and Collaboration, Springer International Publishing, 62-73, 18 May 2017
In book

Visual Impression Generation System Based on Boids Algorithm
Masafumi Ishii; Jinhwan Kwon; Keiki Takadama; Maki Sakamoto
Proceedings of the AAAI 2017 Spring Symposium on Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing Technical Report SS-17-08, 681-686, 28 Mar. 2017, Peer-reviwed
International conference proceedings, English

Affinity Based Search Amount Control in Decomposition Based Evolutionary Multi-Objective Optimization
Hiroyuki Sato; Minami Miyakawa; Keiki Takadama
10th EAI International Conference on Bio-inspired Information and Communications Technologies (BICT), in USB memory, Mar. 2017, Peer-reviwed
International conference proceedings, English

Wellbeing AI Invited Speaker Abstracts.
Takashi Kido; Keiki Takadama
2017 AAAI Spring Symposia, AAAI Press, 2017
International conference proceedings

Wellbeing AI invited speaker abstracts
Takashi Kido; Keiki Takadama
AAAI Spring Symposium - Technical Report, SS-17-01 - SS-17-08, 751-752, 2017, This paper contains the invited speaker abstracts from the Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing 2017 AAA1 Spring Symposium held at Stanford University, March 27-29.
International conference proceedings

Directional Pareto Front and Its Estimation to Encourage Multi-objective Decision-Making
Takagi, M; Takadama, K; Sato, H
IEEE Access, 11, 20619-20634, 2017, Peer-reviwed

Optimization of Aircraft Landing Route and Order Based on Novelty Search
Akinori Murata; Hiroyuki Sato; Keiki Takadama
Last, INTELLIGENT AND EVOLUTIONARY SYSTEMS, IES 2016, SPRINGER INT PUBLISHING AG, 8, 291-304, Nov. 2016, Peer-reviwed, This paper focuses on the Aircraft Landing Problem (ALP) and proposes the efficient aircraft landing route and order optimization method compared to the conventional method. As a difficulty in solving ALP, both landing route and order of all aircrafts should be optimized together, meaning that they cannot be optimized independently. To tackle this problem, our method employs novelty search to generate variety candidates of aircraft landing routes, which are indispensable to generate the feasible landing order of all aircraft. Through the experiment on a benchmark problem, it has revealed that the proposed method can reduce the occupancy time of aircrafts in an airport.
International conference proceedings, English

Communication-Less Cooperative Q-Learning Agents in Maze Problem
Uwano Fumito; Takadama Keiki
Last, INTELLIGENT AND EVOLUTIONARY SYSTEMS, IES 2016, 8, 453-467, Nov. 2016, Peer-reviwed

Deployment of Wireless Mesh Network Using RSSI-Based Swarm Robots:Passing Narrow Corridor by Movement Function along Walls
Hattori, K; Tatebe, N; Kagawa, T; Owada, Y; Shan, L; Temma, K; Hamaguchi, K; Takadama, K
Last, Journal Artificial Life and Robotics, Springer, Vol.21, No.4, 434-442, 01 Sep. 2016
English

Directional Repair in Evolutionary Optimization of m-Objective k-Knapsack Problems
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
Journal of Signal Processing, Research Institute of Signal Processing, Japan, 20, 4, 161-164, Jul. 2016, Peer-reviwed, To solve m-objective k-knapsack problems (mk-KPs) by using evolutionary algorithms, we propose a repair method that transforms infeasible solutions into feasible ones. In evolutionary multi-objective optimization, each solution in the population has a role in approximating a part of the Pareto front. However, since the conventional weighted scalar repair method (WSR) does not consider the position of each solution in the objective space, the solution diversity to approximate a wide range of the Pareto front is deteriorated. To improve the search performance of evolutionary algorithms for solving mk-KPs by enhancing the diversity of solutions, we propose a repair method considering the positions and repair directions of infeasible solutions in the objective space. Experimental results show that the proposed method improves the diversity of solutions and achieves higher search performance than the conventional WSR in mk-KPs.
Scientific journal, English

Adaptive Learning Based on Genetic Algorithm for Rover in Planetary Exploration
Fumito Uwano; Akinori Murata; Keiki Takadama
Last, Proceedings of The International Symposium on Artificial Intelligence, Robotics and Automation in Space, i-SAIRAS 2016, Jun. 2016, Peer-reviwed
International conference proceedings, English

Optimization of aircraft landing route and order: An approach of hierarchical evolutionary computation
Akinori Murata; Masaya Nakata; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
BICT 2015 - 9th EAI International Conference on Bio-Inspired Information and Communications Technologies, Association for Computing Machinery, Inc, 2, 6, 1-8, 24 May 2016, Peer-reviwed, This paper focuses on the aircraft landing optimization problem where both the landing routes and the landing order of aircrafts should be optimized to minimize an occupancy time of airport, and proposes its optimization method which is robust to dynamical situations such as weather conditionchange and other aircrafts'landing routes change. As a difficulty of this optimization problem, appropriate landing routes of aircrafts change depending on such an environment change. To tackle this problem,this paper proposes the hierarchical evolutionary computation to solve the aircraft landing optimization problem. Specifically, our method firstly generates candidates of main landing route of all aircrafts with their own additional sub-routes, which can be applied into the main routes depending on the current environmental situation. Secondly, our method evolves the good combination of landing routes (including their sub-routes) of all aircrafts to minimize an occupancy time of airport. Through the intensive experiment on a benchmark problem, the following implications have been found: (1) our method successfully generates robust landing routes including some sub-routes,which are flexible depending on environmental situations
and (2) Our method can finds an adequate landing order which contributes to reducing the occupancy time.
International conference proceedings, English

Reinforcement learning with internal reward for multi-Agent cooperation: A theoretical approach
Fumito Uwano; Naoki Tatebe; Masaya Nakata; Keiki Takadama; Tim Kovacs
BICT 2015 - 9th EAI International Conference on Bio-Inspired Information and Communications Technologies, Association for Computing Machinery, Inc, 16, 8, 1-8, 24 May 2016, Peer-reviwed, This paper focuses on a multi-Agent cooperation which is generally difficult to be achieved without sufficient information of other agents, and proposes the reinforcement learning method that introduces an internal reward for a multi-Agent cooperation without sufficient information. To guarantee to achieve such a cooperation, this paper theoretically derives the condition of selecting appropriate actions by changing internal rewards given to the agents, and extends the reinforcement learning methods (Q-learning and Profit Sharing) to enable the agents to acquire the appropriate Q-values up- dated according to the derived condition. Concretely, the internal rewards change when the agents can only find better solution than the current one. The intensive simulations on the maze problems as one of test beds have revealed the following implications:(1) our proposed method successfully enables the agents to select their own appropriate cooperating actions which contribute to acquiring the minimum steps towards to their goals, while the conventional methods (i.e., Q-learning and Profit Sharing) cannot always acquire the minimum steps
and (2) the proposed method based on Profit Sharing provides the same good performance as the proposed method based on Q-learning.
International conference proceedings, English

Generation of Public Transportation Network for Commuter Stranded Problem
Takahiro Majima; Keiki Takadama; Daisuke Watanabe; Mitujirou Katuhara
WEIN (Workshop on Emergent Intelligence on Networked Agents) 2016 Proceedings, May 2016, Peer-reviwed

Promoting Machine-code Program Evolution in Asynchronous Genetic Programming
Harada, T; Takadama, K; Sato H
SICE Journal of Control, Measurement, and System Integration (JCMSI), The Society of Instrument and Control Engineers, 9, 2, 93-102, 31 Mar. 2016, Peer-reviwed, This paper focuses on an asynchronous program evolution in evolutionary computation, which is hard to evolve programs effectively unlike a synchronous program evolution that evolves individuals effectively by selecting good parents after evaluations of all individuals in each generation. To tackle this problem, we explore the mechanism that can promote an asynchronous program evolution by selecting a good individual without waiting for evaluations of all individuals. For this purpose, this paper investigates the effectiveness of the proposed mechanisms in genetic programing (GP) domain by evaluating it in the two types of problems, the arithmetic and the Boolean problems. Through the intensive experiments of the eight kinds of testbeds under the two types of problems, the following implications have been revealed: (1) the program asynchronously evolved with the proposed mechanism can be completed with mostly the same or shorter execution steps than the program asynchronously evolved without the proposed mechanism, in particular the proposed mechanism improves the performance of the asynchronous evolution in the arithmetic problems; and (2) the program asynchronously evolved with the proposed mechanism can be completed with mostly the same or shorter execution steps than the program evolved by the conventional GP.
Scientific journal, English

Characteristic and Application of Network Evolution Model for Public Transport Network
Majima, T; Takadama, K; Watanabe, D; Katsuhara, M
Multiagent and Grid Systems - An International Journal, 12, 1, 1-11, 08 Mar. 2016, Peer-reviwed
Scientific journal, English

Controlling selection areas of useful infeasible solutions for directed mating in evolutionary constrained multi-objective optimization
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE, SPRINGER, 76, 1-2, 25-46, Feb. 2016, Peer-reviwed, As an evolutionary approach to solve constrained multi-objective optimization problems (CMOPs), recently an algorithm using the two-stage non-dominated sorting and the directed mating (TNSDM) was proposed. In TNSDM, the directed mating utilizes infeasible solutions dominating feasible solutions in the objective space to generate offspring. The directed mating significantly contributes to the search performance improvement in evolutionary constrained multi-objective optimization. However, the conventional directed mating has two problems. First, since the conventional directed mating selects a pair of parents based on the conventional Pareto dominance, two parents having different search directions may be mated. Second, the directed mating cannot be performed in some cases especially when the population has few useful infeasible solutions. In this case, the conventional mating using only feasible solutions is performed instead. Thus, the effectiveness of the directed mating cannot always be achieved depending on the number of useful infeasible solutions. To overcome these problems and further enhance the effect of the directed mating in TNSDM, in this work we propose a method to control the selection area of useful infeasible solutions by controlling dominance area of solutions (CDAS). We verify the effectiveness of the proposed method in TNSDM, and compare its search performance with the conventional CNSGA-II on discrete m-objective k-knapsack problems and continuous mCDTLZ problems. The experimental results show that the search performance of TNSDM is further improved by controlling the selection area of useful infeasible solutions in the directed mating.
Scientific journal, English

Crater Detection Method using Principal Component Analysis and its Evaluation
TAKINO Tatsuya; NOMURA Izuru; MORIBE Misako; KAMATA Hiroyuki; TAKADAMA Keiki; FUKUDA Seisuke; SAWAI Shujiro; SAKAI Shin-ichiro
TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES, AEROSPACE TECHNOLOGY JAPAN, THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES, 14, 30, Pt_7-Pt_14, 2016,

In this study, a crater detection method for a moon-landing system with low computational resources is proposed. The proposed method is applied to the Smart Lander for Investigating Moon (SLIM), which aims for a pin-point landing on the moon. According to this plan, surface images of the moon will be captured by a camera mounted on the space probe, and the craters are to be detected from the images. Based on the positional relationship between detected craters, the method estimates the exact flight position of the space probe. Because the computational resources of SLIM are limited, rapid and accurate crater detection must be performed using fixed-point arithmetic on a field-programmable gate array (FPGA). This study proposes a crater detection method that uses principal component analysis (PCA). The computational processing for crater detection by PCA is performed by product-sum operations, which are suitable for fixed-point arithmetic. Moreover, this method is capable of parallel processing; hence high-speed processing is expected. This study not only introduces a crater detection method using PCA but also evaluates the properties of this method.

English

Preventing Incorrect Opinion Sharing with Weighted Relationship Among Agents
Rei Saito; Masaya Nakata; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
Last, Human Interface and the Management of Information: Applications and Services, Pt II, SPRINGER INT PUBLISHING AG, 9735, 50-62, 2016, Peer-reviwed, This paper aims at investigating how correct or incorrect opinions are shared among the agents in the weighted network where the relationship among the agent (as nodes of its network) is different each other, and exploring how the agents can be promoted to share only correct opinions by preventing to acquire the incorrect opinions in the weighted network. For this purpose, this paper focuses on Autonomous Adaptive Tuning algorithm (AAT) which can improve an accuracy of correct opinion shared among agents in the various network, and improves it to address the situation which is close in the real world, i.e., the relationship among agents is different each other. This is because the original AAT does not consider such a different relationship among the agents. Through the intensive empirical experiments, the following implications have been revealed: (1) the accuracy of the correct opinion sharing with the improved AAT is higher than that with the original AAT in the weighted network; (2) the agents in the improved AAT can prevent to acquire incorrect opinion sharing in the weighted network, while those in the original AAT are hard to prevent in the same network.
International conference proceedings, English

XCS-DH: Minimal Default Hierarchies in XCS
Tim Kovacs; Simon Rawles; Larry Bull; Masaya Nakata; Keiki Takadama
2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 4747-4754, 2016, Peer-reviwed, A default hierarchy is set of rules containing one or more exceptions to one or more default rules e.g. all dogs are friendly, except my neighbour's. Default hierarchies were the subject of considerable interest in early Learning Classifier Systems research, but they were abandoned due to the considerable difficulty of solving the credit assignment problems they involve. The most popular Learning Classifier System, XCS, and its derivatives do not support default hierarchies because in XCS each rule must be accurate, whereas in a default hierarchy an overgeneral rule may be overridden by a correct rule. In this work we enable XCS to evolve minimal default hierarchies by allowing two conditions in one rule, but evaluating only the accuracy and fitness of the whole as a whole. This simple step avoids the credit assignment issues faced by earlier systems. We call this XCS-DH. Preliminary evaluation of XCS-DH on a number of Boolean functions indicates a strong tendency to exploit the increased expressiveness of its rules. On some functions we observe slower learning and a larger population size, which we attribute to the increased rule expressiveness, which increases the search space. However, we also observe that in a problem that is particularly suitable for XCS-DH representation, and that is sufficient difficult for XCS, XCS-DH's learning rate is faster than XCS's. We take this as confirmation of the potential of learning default hierarchies with XCS-DH.
International conference proceedings, English

Enhanced Decomposition-Based Many-Objective Optimization Using Supplemental Weight Vectors
Hiroyuki Sato; Satoshi Nakagawa; Minami Miyakawa; Keiki Takadama
2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 1626-1633, 2016, Peer-reviwed, In evolutionary multi-objective optimization, each solution in the population generally has two roles. The first one is to approximate a part of the Pareto front, and the second one is to be a variable information resource to generate offspring. In many-objective optimization involving four or more conflicting objectives, solutions in the population have to be sparsely distributed in the objective space and the variable space to approximate a high-dimensional Pareto front, and each solution faces the difficulty to play the second role since variables are drastically individualized in the population. To overcome this problem, we focus on MOEA/D algorithm framework and propose a method to introduce supplemental weight vectors and solutions which maintain variable information resource to enhance the solution search for each part of the Pareto front. Experimental results using many-objective knapsack problems show that the supplemental weight vectors and solutions improves the search performance of MOEA/D by improving the diversity of the obtained solutions.
International conference proceedings, English

Personalized Real-Time Sleep Stage from Past Sleep Data to Today's Sleep Estimation
Yusuke Tajima; Tomohiro Harada; Hiroyuki Sato; Keiki Takadama
Last, Human Interface and the Management of Information: Applications and Services, Pt II, SPRINGER INT PUBLISHING AG, 9735, 501-510, 2016, Peer-reviwed, This paper focuses on the real-time sleep stage estimation and proposes the method which appropriately selects the past sleep data as the prior knowledge for improving accuracy of the sleep stage estimation. The prior knowledge in this paper is represented as the parameters for estimating the sleep stage and it is composed of 26 parameters which give an influence to the accuracy of the real-time sleep stage estimation. Concretely, these parameters are acquired from the heartbeat data of a certain past day, and they are used to estimate the heartbeat data of a current day, which data is finally converted to the sleep stage. The role of the proposed method is to select the appropriate parameters of the heartbeat data of a certain past day, which is similar to the heartbeat data of a current day. To investigate the effectiveness of the proposed method, we conducted the human subject experiment which investigated the accuracy of the real-time sleep stage estimation of two adult males ( whose age are 20 and 40) and one adult female ( whose age is 60) by employing the appropriate parameters of the different day from three days. The experimental results revealed that the accuracy of the real-time sleep stage estimation with the proposed method is higher than that without it.
International conference proceedings, English

Evolutionary Algorithmic Parameter Optimization of MOEAs for Multiple Multi-Objective Problems
Motoaki Kakuguchi; Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
2016 JOINT 8TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS (SCIS) AND 17TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (ISIS), IEEE, 30-35, 2016, Peer-reviwed, Nowadays, algorithmic studies of multi-objective evolutionary algorithms (MOEAs) are flooded with too many search algorithms. Each MOEA has its own expert problem domain. To clarify not only the optimal MOEA and its parameters for each of multiple multi-objective optimization problems (MOPs) but the robust MOEAs for multiple MOPs, this work proposes a meta-MOEA framework to search the Pareto optimal algorithmic parameters for multiple MOPs. In this work, we use two DTLZ2 benchmark problems with 2 and 4 objectives and optimize the base algorithm, the crossover rate and its parameter, the mutation rate and its parameter for the both DTLZ2 problems by the meta-MOEA. The experiment results show that the optimal algorithmic parameters for each of two DTLZ2 problems are different and the robust algorithmic parameters for both problems can be obtained by the meta-MOEA framework.
International conference proceedings, English

Learning Classifier System with Deep Autoencoder
Kazuma Matsumoto; Rei Saito; Yusuke Tajima; Masaya Nakata; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 4739-4746, 2016, Peer-reviwed, This paper proposes a novel Learning Classifier System (LCS) which integrates Deep AutoEncoder named DAE to solve high-dimensional problems. In the proposed LCS, DAE starts to compress (encode) an environmental input as a high-dimensional information to an input of LCS as a low-dimensional information and decompresses (decodes) an output of LCS as a low-dimensional information to a system output as a high-dimensional information. Since the compressed inputs are encoded by real value, this paper employs XCSR (i.e., an LCS with real value coding) and combines XCSR with DAE. In order to investigate the effectiveness of the proposed LCS, XCSR with DAE, this paper conducts the preliminary experiment on the benchmark classification problem, i.e., 6-Multiplexer problem. The intensive experiments on the compression from 6 to 5 bits have revealed the following implications: (1) XCSR with DAE performs as well as XCSR even learning from the compressed input data; and (2) XCSR with DAE successfully decodes the compressed rules to extract the rules which are the same as those learned with not compressed input data.
International conference proceedings, English

Extracting Different Abstracted Level Rule with Variance-Based LCS.
Caili Zhang; Takato Tatsumi; Masaya Nakata; Keiki Takadama; Hiroyuki Sato; Tim Kovacs
2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS), Sapporo, Japan, August 25-28, 2016, IEEE, 160-165, 2016, Peer-reviwed

Variance-based Learning Classifier System without Convergence of Reward Estimation.
Takato Tatsumi; Takahiro Komine; Masaya Nakata; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
Last, Genetic and Evolutionary Computation Conference, GECCO 2016, Denver, CO, USA, July 20-24, 2016, Companion Material Proceedings, ACM, 67-68, 2016, Peer-reviwed

A modified cuckoo search algorithm for dynamic optimization problems.
Yuta Umenai; Fumito Uwano; Yusuke Tajima; Masaya Nakata; Hiroyuki Sato; Keiki Takadama
Last, IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, BC, Canada, July 24-29, 2016, IEEE, 1757-1764, 2016, Peer-reviwed

Effective Deployment of Wireless Mesh Network Using Mobile Robots Based on RSSI:—Performance Evaluation from the View Point of Movement Impossibility Rate—
TATEBE Naoki; HATTORI Kiyohiko; KAGAWA Toshinori; OWADA Yasunori; HAMAGUCHI Kiyoshi; TAKADAMA Keiki
Transactions of the Society of Instrument and Control Engineers, The Society of Instrument and Control Engineers, 52, 3, 160-171, 2016, Peer-reviwed, This paper propose a novel deployment method of Wireless Mesh Network (WMN) using a group of mobile robots equipped with a wireless transceiver. Our proposed method utilize rough relative positions of the robots estimated by Radio Signal Strength Indicators (RSSI) to deploy WMN. Our algorithm is consist of following two parts; (1) Fully distributed and dynamic role decision method among robots and (2) Adaptive direction control using time difference of RSSI. In this research, we evaluate performance of proposed and conventional methods which use RSSI at simulated disaster area including radio-wave propagation and wireless communication protocol models. As a result of simulations and real robots experiments, our proposed method outperformed conventional methods in aspects of required deployment time and traveled distance of the robots.
Scientific journal, Japanese

Multi-agent based Bus Route Optimization for Restricting Passenger Traffic Bottlenecks in Disaster Situations
Morimoto, S; Jinba, T; Kitagawa, H; Takadama, K; Majima, T; Watanabe, D; Katuhara, M
International Journal of Automation and Logistics (IJAL), 2, 1/2, 153-177, 2016, Peer-reviwed
Scientific journal, English

Awareness based recommendation - passively interactive learning system
Yamaguchi, T; Nishimura, T; Takadama, K
International Journal of Robotics Applications and Technologies (IJRAT), Vol.4, issue 1, 83-99, 2016, Peer-reviwed
Scientific journal, English

Evolutionary Multi-Objective Route and Fleet Assignment Optimization for Regular and Non-Regular Flight
Takadama, K; Jinba, T; Harada, T; Sato, H
International Journal of Automation and Logistics (IJAL), 2, 1/2, 122-152, 2016, Peer-reviwed
Scientific journal, English

許容誤差を自己適応可能な学習分類子システム
辰巳 嵩豊; 小峯 嵩裕; 中田 雅也; 佐藤 寛之; 高玉 圭樹
Last, 進化計算学会論文誌, The Japanese Society for Evolutionary Computation, Vol. 6, No. 2, 90-103, 02 Nov. 2015, Peer-reviwed, The XCS classifier system is designed to evolve accurately generalized classifiers as an optimal solution to a problem. All classifiers are identified as either accurate or inaccurate on the basis of a pre-defined parameter called an accuracy criterion. Previous results suggested a standard setting of the accuracy criterion robustly performs on multiple simple problems so XCS evolves the optimal solution. However, there lacks a guideline of reasonable setting of accuracy criterion. This causes a problem that the accuracy criterion should be empirically customized for each complex problems especially noisy problems which is a main focus of this paper. This paper proposes a self-adaptation technique for the accuracy criterion which attempts to enable XCS to evolve the optimal solution on the noisy problems. In XCS-SAC(XCS with Self-Adaptive accuracy criterion), each classifier has its own accuracy criterion in order to find an adequate setting of accuracy criterion for each niche. Then, each classifier's accuracy criterion is updated with the variance of reward which its classifier has received. We test XCS-SAC on a benchmark classification problem (i.e., the multiplexer problem) with noise (the Gaussian noise and alternative noise). Experimental results show XCS-SAC successfully solves the noisy multiplexer problems as well as XCS but evolves a more compact solution including an optimal solution than XCS.
Scientific journal, Japanese

時系列歩行データに基づく環境知能型摺り足検知システム：転倒検知から転倒察知へ
高玉 圭樹; 小峯 嵩裕
人工知能学会誌, 人工知能学会, Vol. 30, No. 6, 733-738, 01 Nov. 2015, Peer-reviwed
Scientific journal, Japanese

Generating Hub-Spoke Network for Public Transportation
Takahiro Majima; Keiki Takadama; Daisuke Watanabe; Mitujirou Katuhara
SWARM 2015: The First International Symposium on Swarm Behavior and Bio-Inspired Robotics, 48-51, Oct. 2015, Peer-reviwed

Wireless Mesh Network Deployment by Real SWARM Robots based on RSSI
Naoki Tatebe; Kiyohiko Hattori; Toshinori Kagawa; Yasunori Owada; Kiyoshi Hamaguchi; Keiki Takadama
The First International Symposium on Swarm Behavior and Bio-Inspired Robotics SWARM), Oct. 2015, Peer-reviwed
International conference proceedings, English

XCS-SL: a rule-based genetic learning system for sequence labeling
Masaya Nakata; Tim Kovacs; Keiki Takadama
Evolutionary Intelligence, Springer Verlag, 8, 2-3, 133-148, 26 Sep. 2015, Peer-reviwed, Sequence labeling is an interesting classification domain where, like normal classification, every input has a class label, but unlike normal classification, prediction of an input’s label may depend on the values of other inputs or their classes, and so a learner may need to refer to inputs and classes at different time stamps to classify the current input. This is more difficult because a learner does not know where and how many inputs are needed to classify the current input. Our interest is in learning general rules for sequence labeling. The XCS algorithm is a rule-based knowledge discovery system powered by a genetic algorithm which has often been used for classification. Here we present XCS-SL, an extension of XCS classifier system which can be applicable to sequence labeling. Towards an application of Learning Classifier System (LCS) to sequence labeling, we propose a new classifier condition with memory (called a variable-length condition) and a rule-discovery system for the new classifier condition, which enables XCS to apply it to sequence labeling. In XCS-SL, classification rules (called “classifiers” here) can include extra conditions on previous inputs, which act as memories. In sequence labeling, the number of conditions/memories needed may be different for each input, hence, using a fixed number of conditions (i.e., fixed-length condition) for all classifiers is not a good solution. Instead, XCS-SL classifiers have a variable-length condition to provide more or less memory. The genetic algorithm can grow and shrink conditions to find a suitable memory size. On two synthetic benchmark problems XCS-SL learns optimal classifiers, and on a real-world sequence labeling task it derives high classification accuracy and discovers interesting knowledge that shows dependencies between inputs at different times. The comprehensively described system is the first application of a LCS to sequence labeling and we consider it a promising direction for future work.
Scientific journal, English

Rule reduction by selection strategy in XCS with adaptive action map
Masaya Nakata; Pier Luca Lanzi; Keiki Takadama
Evolutionary Intelligence, Springer Verlag, 8, 2-3, 71-87, 26 Sep. 2015, Peer-reviwed, The XCS classifier system is a rule-based evolutionary machine learning system. XCS evolves classifiers in order to learn generalized solutions. The XCS with adaptive action mapping (XCSAM) is inherited from XCS, which evolves a best action map where it evolves classifiers that advocate the best action in every state. Accordingly, XCSAM can potentially evolve solutions that are more compact than XCS, which in contrast focuses on a complete action map. Previous experimental results however have shown that, in some problems, XCSAM may produce solutions with more classifiers than XCS. In this paper, we initially show that the original fitness-based selection strategy of XCS produces non effective classifiers which are not likely to be in the best action map (i.e., they are inaccurate ones or do not have best actions) in XCSAM. Then, we introduce a new selection strategy for XCSAM that promotes the evolution of classifiers advocating the best action map and thus produces more compact solutions. The new strategy selects classifiers based both on their fitness (like XCS) and on the parameter optimality of action of XCSAM. The result is a pressure towards classifiers that are accurate and advocate the best actions. We present analyses showing that the new selection strategy successfully enables XCSAM to focus on classifiers having best actions. Our experimental results show that XCSAM with the new selection strategy (called XCSAM-SS) can evolve smaller solutions than XCS (and the original XCSAM) both in single-step and multi-step problems. As a consequence, XCSAM can also learn with smaller iterations than XCS in the single-step problem. Our conclusion is that, as the best action map potentially has a compact solution, XCSAM evolves a much compact solution than XCS by adding an adequate selection strategy.
Scientific journal, English

Sightseeing plan recommendation system using sequential pattern mining based on adjacent activities
Takuma Fujitsuka; Tomohiro Harada; Hiroyuki Sato; Keiki Takadama; Tomohiro Yamaguchi
2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015, Institute of Electrical and Electronics Engineers Inc., 08 Sep. 2015, Peer-reviwed, This study proposes the novel sightseeing plan recommendation system based on the ad jacency of the activities in the plans (e.g., a shopping is selected after a lunch), and aims at verifying its effectiveness through the subject experiments. The proposed system recommends the sightseeing plans according to the adjacent relationship of the activities in the plans given by the group of other users whose preference are similar to the user. The intensive subject experiments have revealed that our proposed system can recommend the plans that contains the appropriate order of activities for the sightseeing plan recommendation in comparison with ones recommended by the conventional system.
International conference proceedings, English

Evolutionary algorithm for uncertain evaluation function
Yusuke Tajima; Masaya Nakata; Hiroyasu Matsushima; Yoshihiro Ichikawa; Hiroyuki Sato; Kiyohiko Hattori; Keiki Takadama
Last, New Mathematics and Natural Computation, World Scientific Publishing Co. Pte Ltd, 11, 2, 201-215, 28 Jul. 2015, Peer-reviwed, This paper proposes the evolutionary algorithm (EA) for the uncertain evaluation function in which fitness values change even with the same input. In detail, the proposed method employs the probability model to acquire the appropriate attributes that can drive the good solutions. To investigate the effectiveness of the proposed method, we apply it to sleep stage estimation problem where an accuracy of sleep stage estimation changes even in the same estimation filter (correspondingly the solutions). The experimental results have revealed the following implications: (i) The proposed method succeeded to acquire the robust estimation filters which stably derive a high accuracy of the sleep stage estimation
(ii) in detail, the proposed method with the roulette selection shows higher performance than the one with the random selection
and (iii) the proposed method shows high performance and robustness to the different days in comparison with the conventional sleep stage estimation method.
International conference proceedings, English

Multi-objective optimization for common and special components: First step toward network optimization of regular and non-regular flights
Takahiro Jinba; Hiroto Kitagawa; Eriko Azuma; Keiji Sato; Hiroyuki Sato; Kiyohiko Hattori; Keiki Takadama
Last, New Mathematics and Natural Computation, World Scientific Publishing Co. Pte Ltd, 11, 2, 183-199, 28 Jul. 2015, Peer-reviwed, To optimize the problem composed of (i) the common components which should be optimized from the viewpoint of all objective functions and (ii) the special components which should be optimized from the viewpoint of one of the objective functions, this paper proposes a new multi-objective optimization method which optimizes not only the common components for all objective functions but also the special ones for each objective function. To investigate the effectiveness of the proposed method, this paper tested our method on the test-bed problem which is an extended version of the 0/1 knapsack problem. The intensive experiments have revealed the following implications: (i) Our method finds better solutions which have higher fitness than the conventional method (NSGA-II)
(ii) our method can find the solutions that had a large norm (which corresponds to a high profit of an airline company in the flight scheduling problem) with the high rate of the common components
and (iii) since the crowding distance employed in our method contributes to keeping the diversity during the solution search, our method has high exploration capability of solutions.
International conference proceedings, English

Control of crossed genes ratio for directed mating in evolutionary constrained multi-objective optimization
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Inc, 1201-1204, 11 Jul. 2015, Peer-reviwed, As an evolutionary approach to solve constrained multi-objective optimization problems (CMOPs), an algorithm using the two-stage non-dominated sorting and the directed mating (TNSDM) has been proposed. To generate offspring, the directed mating utilizes useful infeasible solutions having better objective values than feasible solutions in the population. The directed mating achieves higher search performance than the conventional mating which avoids using infeasible solutions in several CMOPs. However, since the directed mating uses infeasible solutions, generated offspring tend to be infeasible compared with the conventional mating. To further improve the effectiveness of the directed mating by improving the feasibility of generated offspring, in this work we propose a method to control the crossed genes ratio in the directed mating. In this method, we control the amount of genes copied from infeasible parents to offspring in the directed mating. Experimental results using m-objective k-knapsack problem with 2-4 objectives show the contribution of the directed mating for the search performance is further improved by controlling crossed genes ratio.
International conference proceedings, English

Network construction for correct opinion sharing by selecting a curator agent
Rei Saito; Naoki Tatebe; Ryo Takano; Keiki Takadama
2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), IEEE, Jul. 2015
International conference proceedings

Adjusting SLIM Spacecraft Location Estimation to Crater Detection for High Precision and Computational Time Reduction
Kotaro USUI; Tomohiro HARADA; Keiki TAKADAMA; Hiroyuki KAMATA; Seisuke FUKUDA; Shujiro SAWAI; Shinichiro SAKAI
Proceedings of ISTS 30th, The Japan Society for Aeronoutical and Spece Sciences, 2015t, 14, Jul. 2015, Peer-reviwed
International conference proceedings, English

Assembly Language Program Evolution in Tierra-based On-Board Computer through Single Event Upset
Harada, T; Takadama, K
Last, International Journal of Engineering Science and Innovative Technology, Vol.4, No.4, 27-42, Jul. 2015, Peer-reviwed
Scientific journal, English

宇宙ステーション補給機「こうのとり」－輸送機のための荷物配置問題－
高玉 圭樹
Corresponding, 情報処理学会，情報処理, 情報処理学会, Vol.56, No.7, 669-672, 15 Jun. 2015, Peer-reviwed, 本稿では，宇宙ステーション補給機「こうのとり」に搭載するカーゴレイアウトシステムについて紹介し，日本のAI技術がNASAやESAに比べてどのような位置づけにあるのかを議論する．具体的には，カーゴそのものをエージェント（自律主体）として捉え，カーゴ自らが独立にHTVの重心が機体の中心に近づくように移動し，配置を決定するマルチエージェント型システムを紹介し，複数種類あるカーゴの莫大な配置の組合せから最適解を見出す従来方法に対する利点と更なる可能性について言及する．
Scientific journal, Japanese

Characteristic of Passenger's Route Selection and Generation of Public Transport Network
Majima, T; Takadama, K; Watanabe, D; Katsuhara, M
SICE Journal of Control, Measurement, and System Integration (JCMSI, The Society of Instrument and Control Engineers, Vol. 8,, No. 1, 67-73, 21 Jan. 2015, Peer-reviwed, Scheduled liner service is a proper system for mass transportation and it is employed by wide range of transportation modes, such as railway, airline, maritime container shipping and bus. To get more customers, providers of the liner services are required to organize effective routes and networks of the service incorporating the characteristic of the passenger's route selection. This paper tackles to the problem of generating Public Transit Network as one of scheduled liner service. The method generating PTN is based on Multi Agent System that incorporates the characteristic of passenger's route selection. And it is also reported that the developed method successfully output best solution for a benchmark problem.
Scientific journal, English

Estimating Surrounding Symptom Level of Dementia Patient by Sleep Stage
Shingo Tomura; Tomohiro Harada; Hiroyuki Sato; Keiki Takadama; Makoto Aoki
2015 9TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION AND COMMUNICATION TECHNOLOGY (ISMICT), IEEE, 190-194, 2015, Peer-reviwed, This paper explores the methods that can estimate a surrounding symptom level of dementia patients by monitoring the sleep features such as the sleep rhythm (cycle) and the light/deep sleep ratio. For this purpose, we employ Watanabe's method to estimate the sleep stages as the sleep feature, which can be transformed from the heartbeat data acquired by the mattress-based sensor. To explore the dementia estimation methods, we conducted the human subject experiment in the hospital. The analysis of the sleep stage of the dementia patient who has the light cognitive function impairment with repeating good and bad healthy conditions have revealed that the averaged cycles of the REM sleep and the light/deep sleep ratio have a great potential of the evaluation criteria that can estimate a surrounding symptom level of dementia patients.
International conference proceedings, English

Ship Route Evolutionary Optimization of Multiple Ship Companies for Distributed Coordination of Resources
Keiki Takadama; Eriko Azuma; Hiroyuki Sato; Takahiro Majima; Daisuke Watanabe; Mitujiro Katuhara
Lead, 2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 1450-1457, 2015, Peer-reviwed, This paper proposes a ship route evolutionary optimization method for competitive ship companies in the industrial logistic network, where many alliances composed of several ship companies compete with others to acquire their resources (i.e., container) for maximizing their profits. One of the significant issues in the industrial logistic network is to find the best distribution of the resources in all alliances even in a competitive market. For this purpose, this paper explores the ship route optimization method for all competitive alliances, which can find their ship routes having higher profit than their actual routes through a good distributed coordination of resources. The intensive analysis of the results on the Pacific Ocean liner route with the actual data have revealed that the following implications: (1) even in competitive situation, the proposed evolutionary optimization method succeeds to find the ship routes of all alliances which can improve their own profits in comparison with those optimized by the conventional approach and those of actual routes, and (2) the ship routes generated by the proposed method have more anchor ports than the actual ship routes while keeping the ship constraints (e.g., the type of ships that each alliance has), which contributes to obtaining the appropriate resources as a good distributed coordination.
International conference proceedings, English

Multi-agent Based Bus Route Optimization for Restricting Passenger Traffic Bottlenecks in Disaster Situations
Sayaka Morimoto; Takahiro Jinba; Hiroto Kitagawa; Keiki Takadama; Takahiro Majima; Daisuke Watanabe; Mitsujiro Katsuhara
PROCEEDINGS OF THE 18TH ASIA PACIFIC SYMPOSIUM ON INTELLIGENT AND EVOLUTIONARY SYSTEMS, VOL 2, SPRINGER INT PUBLISHING AG, 415-428, 2015, Peer-reviwed, This paper focuses on the passenger traffic bottlenecks occurred in the bus route network in disaster situations and proposes the multi-agent based bus route optimization method to resolve such bottlenecks by generating the networks which can effectively transport many stranded persons including ones who wait around the station as the passenger traffic bottlenecks. For this purpose, the proposed method modifies the bus route networks generated as usual conditions to suitably pass many bus lines to and redistribute the buses among the bus lines according to the number of passengers. The intensive simulations have revealed the following implications: (1) the proposed bus route network optimization method generates the route network which is suitable for passenger traffic bottlenecks; (2) the proposed method decreases a risk of the bottlenecks; and (3) our method transports the passengers faster than those by the conventional one in various virtual disaster situations.
International conference proceedings, English

Multi Objective Optimization for Route Planning and Fleet Assignment in Regular and Non-regular Flights
Takahiro Jinba; Tomohiro Harada; Hiroyuki Sato; Keiki Takadama
Last, PROCEEDINGS OF THE 18TH ASIA PACIFIC SYMPOSIUM ON INTELLIGENT AND EVOLUTIONARY SYSTEMS, VOL 2, SPRINGER INT PUBLISHING AG, 561-575, 2015, Peer-reviwed, To optimize the flight schedule that consists of (1) the regular flight operated on the same day and time through one year and (2) the non-regular flight operated on the different day and time according to month, this paper proposes a new multi-objective fleet assignment method. To investigate the effectiveness of our method, this paper applies it to a test problem of Japanese domestic airport network optimization for two months, off-peak and peak months, using a real-world data. The following extensions are introduced: (1) our method can not only obtain a flight network for each month simultaneously, but also can find a network that has an equivalent profit given by the conventional method; (2) in peak month, our method can find a network that has higher profit than the conventional method; and (3) our method can find a network that has a well-balanced profit between off-peak and peak months.
International conference proceedings, English

Toward Robustness Against Environmental Change Speed by Artificial Bee Colony Algorithm based on Local Information Sharing
Ryo Takano; Tomohiro Harada; Hiroyuki Sato; Keiki Takadama
Last, 2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 1424-1431, 2015, Peer-reviwed, This paper focuses on Artificial Bee Colony (ABC) algorithm in multimodal problems with dynamic environmental change, and proposes the additional improvement of ABC algorithm based on local information sharing (ABC-lis) toward robustness against environmental change speed. The additional improvement is that scout bee's phase is modified to calculate by sigmoid function. To investigate the global search ability of ABC-lis and the additional improvement, we compare these algorithms to 3 case of environmental change speeds. The experimental result revealed that the following implications: (1) ABC-lis cannot always maintains the search capability in any change speed. (2) ABC-lis with the additional improvement is able to exert a high performance at every change speed. (3) The number of bees in each local area is able to be controlled by the novel parameter N-l in ABC-lis with the additional improvement.
International conference proceedings, English

Directed Mating Using Inverted PBI Function for Constrained Multi-Objective Optimization
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 2929-2936, 2015, Peer-reviwed, In evolutionary constrained multi-objective optimization, the directed mating utilizing useful infeasible solutions having better objective function values than feasible solutions significantly contributes to improving the search performance. This work tries to further improve the effectiveness of the directed mating by focusing on the search directions in the objective space. Since the conventional directed mating picks useful infeasible solutions based on Pareto dominance, all solutions are given the same search direction regardless of their locations in the objective space. To improve the diversity of the obtained solutions in evolutionary constrained multi-objective optimization, we propose a variant of the directed mating using the inverted PBI (IPBI) scalarizing function. The proposed IPBI-based directed mating gives unique search directions to all solutions depending on their locations in the objective space. Also, the proposed IPBI-based directed mating can control the strength of directionality for each solution's search direction by the parameter theta. We use discrete m-objective k-knapsack problems and continuous mCDTLZ problems with 2-4 objectives and compare the search performances of TNSDM algorithm using the conventional directed mating and the proposed TNSDM-IPBI using IPBI-based directed mating. The experimental results shows that the proposed TNSDM- IPBI using the appropriate theta* achieves higher search performance than the conventional TNSDM in all test problems used in this work by improving the diversity of solutions in the objective space.
International conference proceedings, English

Artificial Bee Colony Algorithm Based on Local Information Sharing in Dynamic Environment
Ryo Takano; Tomohiro Harada; Hiroyuki Sato; Keiki Takadama
Last, PROCEEDINGS OF THE 18TH ASIA PACIFIC SYMPOSIUM ON INTELLIGENT AND EVOLUTIONARY SYSTEMS, VOL 1, SPRINGER INT PUBLISHING AG, 627-641, 2015, Peer-reviwed, This paper focuses on Artificial Bee Colony (ABC) algorithm which can utilize global information in the static environment and extends it to ABC algorithm based on local information sharing (ABC-lis) in dynamic environment. In detail, ABC-lis algorithm shares only local information of solutions unlike the conventional ABC algorithm. To investigates the search ability and adaptability of ABC-lis algorithm to environmental change, we compare it with the conventional two ABC algorithms by applying them to a multimodal problem with dynamic environmental change. The experimental results have revealed that the proposed ABC-lis algorithm can maintain the search performance in the multimodal problem with the dynamic environmental change, meaning that ABC-lis algorithm shows its search ability and adaptability to environmental change.
International conference proceedings, English

Control of Variable Exchange Probability for Directed Mating in Evolutionary Constrained Multi-Objective Continuous Optimization
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
2015 3RD INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL AND BUSINESS INTELLIGENCE (ISCBI 2015), IEEE, 89-94, 2015, Peer-reviwed, For solving constrained multi-objective optimization problems, an evolutionary algorithm using the two-stage non-dominated sorting and the directed mating (TNSDM) has been proposed. To generate offspring, the directed mating utilizes useful infeasible solutions having better objective values than feasible solutions in the population while conventional approaches avoid to use infeasible solutions as parents. Actually, the directed mating significantly contributes to improving the search performance of evolutionary constrained multi-objective optimization. To cross genes (variables) of selected parents, so far commonly-used crossover operators have been combined with the directed mating. To further improve the effectiveness of the directed mating in continuous problems, in this work we propose a method to control the amount of variables inherited from useful infeasible parents by varying the variable exchange probability in the SBX-based variation. Experimental results using two benchmark problems, TNK and mCDTLZ, with 2-4 objectives show that the effectiveness of the directed mating in continuous problems is further improved by increasing variables inherited from useful infeasible parents.
International conference proceedings, English

Extracting both generalized and specialized knowledge by XCS using Attribute Tracking and Feedback.
Keiki Takadama; Masaya Nakata
Corresponding, IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, May 25-28, 2015, IEEE, 3034-3041, 2015, Peer-reviwed

How should Learning Classifier Systems cover a state-action space?
Masaya Nakata; Pier Luca Lanzi; Tim Kovacs; Will Neil Browne; Keiki Takadama
IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, May 25-28, 2015, IEEE, 3012-3019, 2015, Peer-reviwed

Detecting shoplifting from customer behavior data by extended XCS-SL: Towards feature extraction on class-imbalanced sequence data.
Minato Sato; Kotaro Usui; Masaya Nakata; Keiki Takadama
Last, IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, May 25-28, 2015, IEEE, 2981-2988, 2015, Peer-reviwed

Optimization of Aircraft Landing Route and Order: An approach of Hierarchical Evolutionary Computation.
Akinori Murata; Masaya Nakata; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
BICT 2015, Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), New York City, United States, December 3-5, 2015, ICST/ACM, 340-347, 2015, Peer-reviwed

Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach.
Fumito Uwano; Naoki Tatebe; Masaya Nakata; Keiki Takadama; Tim Kovacs
BICT 2015, Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), New York City, United States, December 3-5, 2015, ICST/ACM, 332-339, 2015, Peer-reviwed

Analyzing Human's Continuous Learning Processes with the Reflection sub Task
Tomohiro Yamaguchi; Kouki Takernori; Yuki Tamai; Keiki Takadama
2015 10TH ASIAN CONTROL CONFERENCE (ASCC), IEEE, Vol. 12, No. 1, 20-27, 2015, Peer-reviwed, This paper reports our learning support system for a human learner to visualize his/her mental learning processes with invisible mazes for continuous learning. The objective of this research is to bring the learning ability of the learning agent close to that of a human. To fill in the missing piece of reinforcement learning whose learning process is mainly behavior change, we add two mental learning processes, awareness as pre-learning process and reflection as post-learning process. To observe mental learning processes of a human, we propose a new method for visualizing mental learning processes by the reflection subtask with invisible mazes. As the experimental results, there is a strong negative correlation between the number of discovered solutions of the continuous learning task and the reflection cost. It suggests that subjects whose number of discovered solutions is upper do a very good job of the reflection subtask.
International conference proceedings, English

Evolutionary Multi-Objective Route and Fleet Assignment Optimization for Regular and Non-Regular Flight
Takadama, K; Jinba, T; Harada, T; Sato, H
International Journal of Automation and Logistics (IJAL), to appear, 2015, Peer-reviwed
International conference proceedings, English

Reports of the 2015 AAAI Spring Symposium Series,''
Agarwal, N; Andrist, S; Bohus, D; Fang, F; Fenstermacher, L; Kagal, L; Kido, T; Kiekintveld, C; Lawless, W. F; Liu, H; McCallum, A; Purohit, H; Seneviratne, O; Takadama, K; Taylor, G
AI magazine,, 36, 3, 113-119, 2015, Peer-reviwed
Scientific journal, English

Application of Community Detection Method to Generating Public Transport Network
Takahiro Majima; Keiki Takadama; Daisuke Watanabe; Mitujirou Katuhara
BICT 2014(8th International Conference on Bio-inspired Information and Communications Technologies), Dec. 2014, Peer-reviwed

Compact Genetic Algorithmを導入した学習分類子システムによる分類子数の削減
中田 雅也; ピエール・ルカ・ランチ; 田島 友祐; 高玉 圭樹
情報処理学会論文誌、数理モデル化と応用, Information Processing Society of Japan (IPSJ), 7, 2, 1-16, 27 Nov. 2014, Peer-reviwed, This paper proposes a novel probability model based rule-discovery mechanism using Compact Genetic Algorithm for Learning Classifier System (LCS), which evolves classifiers based on an extracted attribute of classifier conditions, to reduce a size of classifiers are needed in LCS. The proposed rule-discovery mechanism can 1) generate good classifiers that conditions have good building blocks; and 2) solve both single-step problems and multi-step problems where conventional probability-model based rule discovery mechanisms are hard to be applied. This paper applies LCS with the proposed rule-discovery mechanism to both a single-step problem (the multiplexer problem) and a multi-step problem (the grid world problem). Experimental results show following implications: 1) the proposed LCS can reach optimal performances faster than a conventional LCS; and 2) it can reduce the size of classifiers by at least 49% of that of the conventional LCS. Our conclusion is that the proposed rule-discovery mechanism can generate optimal classifiers with fewer generations than the conventional rule-discovery mechanism, and that it can control generating inaccurate classifiers toward the rule reduction.
Scientific journal, Japanese

Multiagent-based ABC Algorithm for Dynamical Environment: Toward Cooperation among Autonomous Rescue Agents
TAKANO Ryo; YAMAZAKI Daichi; ICHIKAWA Yoshihiro; HATTORI Kiyohiko; TAKADAMA Keiki
Last, Computer Software, Japan Society for Software Science and Technology, 31, 3, 3_187-3_199, Jul. 2014, This paper proposes Multiagent-based Artificial Bee Colony (M-ABC) algorithm by improving ABC algorithm for dynamical environments without using global information (i.e., local information only), and investigates its effectiveness on cooperation among rescue agents in dynamic disaster environments, which requires to quickly and efficiently find victims. Intensive simulations on the victim rescue in RoboCup Rescue Simulation System (RCRSS) have revealed the following implications: (1) M-ABC algorithm can rescue victims faster than the full search method as the conventional method. In particular, M-ABC distance (as one of the proposed M-ABC algorithms) can derive the highest performance; (2) M-ABC distance can keep high performance even in dynamical environments where victims move elsewhere; and (3) M-ABC distance can completely rescue victims in dynamical environments, while Ri-one method as the 2012 champion of RoboCup Rescue Simulation League (RCRSL) cannot in such a case.
Japanese

Archives-holding XCS Classifier System: A preliminary study.
Takahiro Komine; Masaya Nakata; Keiki Takadama
Last, 2014 Sixth World Congress on Nature and Biologically Inspired Computing, NaBIC 2014, Porto, Portugal, July 30 - August 1, 2014, IEEE, 53-58, Jul. 2014, Peer-reviwed

Asynchronously Evolving Solutions with Excessively Different Evaluation Time by Reference-based Evaluation
Tomohiro Harada; Keiki Takadama
Last, GECCO'14: PROCEEDINGS OF THE 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, ASSOC COMPUTING MACHINERY, 911-918, Jul. 2014, Peer-reviwed, The asynchronous evolution has an advantage when evolving solutions with excessively different evaluation time since the asynchronous evolution evolves each solution independently without waiting for other evaluations, unlike the synchronous evolution requires evaluations of all solutions at the same time. As a novel asynchronous evolution approach, this paper proposes Asynchronous Reference-based Evaluation (ARE) that asynchronously selects good parents by the tournament selection using reference solution in order to evolve solutions through a crossover of the good parents. To investigate the effectiveness of ARE in the case of evolving solutions with excessively different evaluation time, this paper applies ARE to Genetic Programming (GP), and compares GP using ARE (ARE-GP) with GP using (mu + lambda) selection (mu + lambda)-GP) as the synchronous approach in particular situation where the evaluation time of individuals differs from each other. The intensive experiments have revealed the following implications: (1) ARE-GP greatly outperforms (mu+lambda)-GP from the viewpoint of the elapsed unit time in the parallel computation environment, (2) ARE-GP can evolve individuals without decreasing the searching ability in the situation where the computing speed of each individual differs from each other and some individuals fail in their execution.
International conference proceedings, English

動的環境適応のためのマルチエージェント型ABC アルゴリズム：レスキューエージェント間協調への展開
高野 諒; 山崎大地; 市川嘉裕; 服部聖彦; 高玉圭樹
Last, 日本ソフトウェア科学会, 31, 3, 187-199, Jul. 2014, Peer-reviwed
Scientific journal, Japanese

Evaluating an Integration of Spacecraft Location Estimation with Crater Detection: Toward Smart Lander for Investigating Moon
Takadama, K; Harada, T; Kamata, H; Ozawa, S; Fukuda, S; Sawai, S
Corresponding, The 12th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS2014), 無, 18 Jun. 2014, Peer-reviwed
International conference proceedings, English

Maintaining, Minimizing, and Recovering Machine Language Program through SEU in On-Board Computer
Harada, T; Takadama, K
Last, The 12th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS2014), 無, 18 Jun. 2014, Peer-reviwed
International conference proceedings, English

Favor Information Presentation and Its Effect for Collective-Adaptive Situation
Asami Mori; Tomohiro Harada; Yoshihiro Ichikawa; Keiki Takadama
Last, HUMAN INTERFACE AND THE MANAGEMENT OF INFORMATION: INFORMATION AND KNOWLEDGE IN APPLICATIONS AND SERVICES, PT II, SPRINGER-VERLAG BERLIN, 8522, 8522, 455-466, Jun. 2014, Peer-reviwed, This paper focuses on favor information among people as the factor to lead a group to "collective-adaptive situation" and explores its effect in "Barnga" as the cross-cultural game which aims at investigating how the players make an appropriate group decision. For this purpose, we propose the "favor marker" which appears as a favor for other players in Barnga system. The subjective experiment results with this system have been revealed that the players in both the system-based communication and face-to-face communication lead the collective-adaptive situation by using the favor markers, while being conscious on the difference of card rules which caused conflicts among players. In detail, the following implications have been found: (1) when the players meet their conflict at the first time, their intentions tend to be appear from their behaviors (e. g. gesture) without using the favor maker in the face-to-face communication, while their intentions are appeared by actively using the favor marker in the system-based communication; (2) after some conflicts, the favor marker in both types of communication showed the effect on making an aware of the difference of the card rules and facilitating behavior affected by such differences, which contributes to deriving a smooth group decision making.
International conference proceedings, English

多次元空間問題における商品属性の関係理解と商品選定の支援
沢田石 祐弥; 原田 智広; 佐藤 寛之; 服部 聖彦; 高玉 圭樹; 山口 智浩
電子情報通信学会誌, 97, 6, 482-491, Jun. 2014, Peer-reviwed
Scientific journal, Japanese

集団適応状態に向けた好意情報の提示とその影響
森 有紗美; 原田 智広; 北川 広登; 高玉 圭樹
Last, 電子情報通信学会誌, 97, 6, 429-438, Jun. 2014, Peer-reviwed
Scientific journal, Japanese

Multiagent-based Sustainable Bus Route Optimization in Disaster
Hiroto Kitagawa; Keiji Sato; Keiki Takadama
Last, 情報処理学会論文誌, 55, 4, 15 Apr. 2014, This paper proposes a multiagent-based route optimization method as a next-generation transportation system to generate a sustainable route network which can transport stranded persons effectively even if the road conditions are changed in a disaster situation. For this purpose, we apply a multiagent approach into the route optimization method where an agent corresponds to one route. Such an approach is very useful in a disaster situation because it is easy to add/delete routes and modify their routes according to the dynamic condition change and constraints. Towards a sustainable route network by multiagent approach, our route optimization method (1) employs the bus stop clustering method to generate clustered routes, (2) introduces a cluster-extension method to connect routes in different clusters and (3) adopts the evaluation function in consideration of damage by a change in the condition of roads. Intensive simulations on Mandl's urban transport benchmark problem have revealed the following implications: (1) the proposed method has succeeded in reducing stranded persons, detour persons, detour time, all of which are caused by road condition changes; (2) detour routes have emerged, which contribute to an increasing network sustainability; and (3) we have succeeded in reducing both the passenger's transportation time and the number of buses in a non-damaged situation.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.22(2014) No.2 (online)DOI http://dx.doi.org/10.2197/ipsjjip.22.235------------------------------This paper proposes a multiagent-based route optimization method as a next-generation transportation system to generate a sustainable route network which can transport stranded persons effectively even if the road conditions are changed in a disaster situation. For this purpose, we apply a multiagent approach into the route optimization method where an agent corresponds to one route. Such an approach is very useful in a disaster situation because it is easy to add/delete routes and modify their routes according to the dynamic condition change and constraints. Towards a sustainable route network by multiagent approach, our route optimization method (1) employs the bus stop clustering method to generate clustered routes, (2) introduces a cluster-extension method to connect routes in different clusters and (3) adopts the evaluation function in consideration of damage by a change in the condition of roads. Intensive simulations on Mandl's urban transport benchmark problem have revealed the following implications: (1) the proposed method has succeeded in reducing stranded persons, detour persons, detour time, all of which are caused by road condition changes; (2) detour routes have emerged, which contribute to an increasing network sustainability; and (3) we have succeeded in reducing both the passenger's transportation time and the number of buses in a non-damaged situation.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.22(2014) No.2 (online)DOI http://dx.doi.org/10.2197/ipsjjip.22.235------------------------------
English

Personalized real-time sleep stage remote monitoring system
Yusuke Tajima; Masaya Nakata; Keiki Takadama
Last, 2014 8TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION AND COMMUNICATION TECHNOLOGY (ISMICT), IEEE, to appear, Apr. 2014, Peer-reviwed, This paper proposes the real-time remote monitoring system for personalized sleep stage. Such a system enables care workers to monitor a sleep stage of aged persons remotely, which is required to support aged persons who tend to wander in midnight and/or who are easily to fall down from their beds. For this purpose, we develop the system that can estimate a sleep stage in real-time from a heartbeat and body movement data acquired by an unconstraint-typed pressure sensor, and such a sleep stage is remotely displayed in tablets of care worker to inform a care timing for aged persons. Intensive human subject experiments have revealed the following implications: (1) the past data for estimating the sleep stage (even in one day data) contributes to estimating the future sleep stage; (2) the precision of the sleep stage estimation increases as the number of past data increases; and (3) such a precision further increases by using either of the past data where a heartbeat rate gradually increases or decreases. (i.e., the past data where a heartbeat rate increases (or decreases) contributes to improving a precision of the sleep stage of a person whose heartbeat rate increases (or decreases)).
International conference proceedings, English

Concierge-based Care Support System for Designing Your Own Lifestyle
Takadama, K
The AAAI 2014 Spring Symposia, Big Data Becomes Personal: Knowledge into Meaning, 69-74, 25 Mar. 2014, Peer-reviwed
International conference proceedings, English

Sleep Stage Estimation Using Synthesized Data of Heart Rate and Body Movement
Tajima, Y; Nakata, M; Harada, T; Sato, K; Takadama, K
The AAAI 2014 Spring Symposia, Big Data Becomes Personal: Knowledge into Meaning, 63-68, 25 Mar. 2014, Peer-reviwed
International conference proceedings, English

Archive of Useful Solutions for Directed Mating in Evolutionary Constrained Multiobjective Optimization
Miyakawa, M; Takadama, K; Sato, H
Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Fuji Technology Press, 18, 2, 221-231, 20 Mar. 2014, Peer-reviwed
Scientific journal, English

Controlling selection area of useful infeasible solutions in directed mating for evolutionary constrained multiobjective optimization
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 8426, 137-152, 2014, Peer-reviwed, As an evolutionary approach to solve multi-objective optimization problems involving several constraints, recently a MOEA using the two-stage non-dominated sorting and the directed mating (TNSDM) has been proposed. In TNSDM, the directed mating utilizes infeasible solutions dominating feasible solutions in the objective space to generate offspring. Our previous work showed that the directed mating significantly contributed to improve the search performance of TNSDM on several benchmark problems. However, the conventional directed mating has two problems. First, since the conventional directed mating selects a pair of parents based on the conventional Pareto dominance, two parents having different search directions are mated in some cases. Second, in problems with high feasibility ratio, since the number of infeasible solutions in the population is low, sometimes the directed mating cannot be performed. Consequently, the effectiveness of the directed mating cannot be obtained. To overcome these problems and further improve the effectiveness of the directed mating in TNSDM, in this work we propose a method to control selection areas of infeasible solutions by controlling dominance area of solutions (CDAS). We verify the effectiveness of the proposed method in TNSDM, and compare its search performance with the conventional CNSGA-II on m objectives k knapsacks problems. As results, we show that the search performance of TNSDM is further improved by controlling selection area of infeasible solutions in the directed mating. © 2014 Springer International Publishing.
International conference proceedings, English

What is needed to promote an asynchronous program evolution in genetic programing?
Keiki Takadama; Tomohiro Harada; Hiroyuki Sato; Kiyohiko Hattori
Lead, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 8426, 227-241, Jan. 2014, Peer-reviwed, Unlike a synchronous program evolution in the context of evolutionary computation that evolves individuals (i.e., programs) after evaluations of all individuals in each generation, this paper focuses on an asynchronous program evolution that evolves individuals during evaluations of each individual. To tackle this problem, we explore the mechanism that can promote an asynchronous program evolution by selecting a good individual without waiting for evaluations of all individuals, and investigates its effectiveness in genetic programming (GP) domain. The intensive experiments have revealed the following implications: (1) the program asynchronously evolved with the proposed mechanism can be completed with the shorter execution steps than the program asynchronously evolved without the proposed mechanism
and (2) the program asynchronously evolved with the proposed mechanism can be completed with mostly the same or shorter execution steps than the program synchronously evolved by the conventional GP. © 2014 Springer International Publishing.
International conference proceedings, English

Multiagent-based sustainable bus route optimization in disaster
Hiroto Kitagawa; Keiji Sato; Keiki Takadama
Journal of Information Processing, Information Processing Society of Japan, 22, 2, 235-242, 2014, Peer-reviwed, This paper proposes a multiagent-based route optimization method as a next-generation transportation system to generate a sustainable route network which can transport stranded persons effectively even if the road conditions are changed in a disaster situation. For this purpose, we apply a multiagent approach into the route optimization method where an agent corresponds to one route. Such an approach is very useful in a disaster situation because it is easy to add/delete routes and modify their routes according to the dynamic condition change and constraints. Towards a sustainable route network by multiagent approach, our route optimization method (1) employs the bus stop clustering method to generate clustered routes, (2) introduces a cluster-extension method to connect routes in different clusters and (3) adopts the evaluation function in consideration of damage by a change in the condition of roads. Intensive simulations on Mandl's urban transport benchmark problem have revealed the following implications: (1) the proposed method has succeeded in reducing stranded persons, detour persons, detour time, all of which are caused by road condition changes
(2) detour routes have emerged, which contribute to an increasing network sustainability
and (3) we have succeeded in reducing both the passenger's transportation time and the number of buses in a non-damaged situation. © 2014 Information Processing Society of Japan.
Scientific journal, English

Controlling Selection Area of Useful Infeasible Solutions and Their Archive for Directed Mating in Evolutionary Constrained Multiobjective Optimization
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
GECCO'14: PROCEEDINGS OF THE 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, ASSOC COMPUTING MACHINERY, 629-636, 2014, Peer-reviwed, As an evolutionary approach to solve constrained multi-objective optimization problems (CMOPs), recently a MOEA using the two-stage non-dominated sorting and the directed mating (TNSDM) has been proposed. In TNSDM, the directed mating utilizes infeasible solutions dominating feasible solutions to generate offspring. Although the directed mating contributes to improve the search performance of TNSDM in CMOPs, there are two problems. First, since the number of infeasible solutions dominating feasible solutions in the population depends on each CMOP, the effectiveness of the directed mating also depends on each CMOP. Second, infeasible solutions utilized in the directed mating are discarded in the selection process of parents (elites) population and cannot be utilized in the next generation. To overcome these problems and further improve the effectiveness of the directed mating in TNSDM, in this work we propose an improved TNSDM introducing a method to control selection area of infeasible solutions and an archiving strategy of useful infeasible solutions for the directed mating. The experimental results on m objectives k knapsacks problems shows that the improved TNSDM improves the search performance by controlling the directionality of the directed mating and increasing the number of directed mating executions in the solution search.
International conference proceedings, English

Complete Action Map or Best Action Map in Accuracy-based Reinforcement Learning Classifier Systems
Masaya Nakata; Pier Luca Lanzi; Tim Kovacs; Keiki Takadama
GECCO'14: PROCEEDINGS OF THE 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, ASSOC COMPUTING MACHINERY, 557-564, 2014, Peer-reviwed, We study two existing Learning Classifier Systems (LCSs): XCS, which has a complete map (which covers all actions in each state), and XCSAM, which has a best action map (which covers only the highest-return action in each state). This allows XCSAM to learn with a smaller population size limit (but larger population size) and to learn faster than XCS on well-behaved tasks. However, many tasks have difficulties like noise and class imbalances. XCS and XCSAM have not been compared on such problems before. This paper aims to discover which kind of map is more robust to these difficulties. We apply them to a classification problem (the multiplexer problem) with class imbalance, Gaussian noise or alternating noise (where we return the reward for a different action). We also compare them on real-world data from the UCI repository without adding noise. We analyze how XCSAM focuses on the best action map and introduce a novel deletion mechanism that helps to evolve classifiers towards a best action map. Results show the best action map is more robust (has higher accuracy and sometimes learns faster) in all cases except small amounts of alternating noise.
International conference proceedings, English

A Modified XCS Classifier System for Sequence Labeling
Masaya Nakata; Tim Kovacs; Keiki Takadama
GECCO'14: PROCEEDINGS OF THE 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, ASSOC COMPUTING MACHINERY, 565-572, 2014, Peer-reviwed, This paper introduces XCS-SL, an extension of XCS for sequence labeling, a form of time-series classification where every input has a class label. In sequence labeling the correct class of an input may depend on data received on previous time stamps, so a learner may need to refer to data at previous time stamps. That is, some classification rules (called "classifiers" here) must include conditions on previous inputs (a kind of memory). We assume the agent does not know how many conditions on previous inputs are needed to classify the current input, and the number of conditions/memories needed may be different for each input. Hence, using a fixed number of conditions is not a good solution. A novel idea we introduce is classifiers that have a variable-length condition to refer back to data at previous times. The condition can grow and shrink to find a suitable memory size. On a benchmark problem XCS-SL can learn optimal classifiers, and on a real-world sequence labeling task, it derived high classification accuracy and discovered interesting knowledge that shows dependencies between inputs at different times.
International conference proceedings, English

Messy Coding in the XCS Classifier System for Sequence Labeling
Masaya Nakata; Tim Kovacs; Keiki Takadama
PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XIII, SPRINGER INT PUBLISHING AG, 8672, 191-200, 2014, Peer-reviwed, The XCS classifier system for sequence labeling (XCS-SL) is an extension of XCS for sequence labeling, a form of time-series classification where every input has a class label. In XCS-SL a classifier condition consists of some sub-conditions which refer back to previous inputs. Each sub-condition is a memory. A condition has n sub-conditions which represent an interval from the current time t(0) to a previous time t(-n). A problem of this representation (called interval coding) is, even if only one input at t(-n) is needed, the condition must consist of n sub-conditions to refer to it. We introduce a messy coding based condition where each sub-condition messily refers to a single previous time. Unlike the original coding, the set of sub-conditions does not necessarily represent an interval, so it can represent compact conditions. The original XCS-SL evolutionary mechanism cannot be used with messy coding and our main innovation is a novel evolutionary mechanism. Results on a benchmark show that, compared to the original interval coding, messy coding results in a smaller population size and does not require as high a population size limit. However, messy coding requires more training with a high population size limit. On a real world sequence labeling task messy coding evolved a solution that achieved higher accuracy with a smaller population size than the original interval coding.
International conference proceedings, English

Multiagent-based ABC algorithm for Autonomous Rescue Agent Cooperation
R. Takano; A. Yamazaki; Y. Ichikawa; K. Hattori; K. Takadama
Last, 2014 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), IEEE, to appear, 585-590, 2014, Peer-reviwed, This paper focuses on cooperation among autonomous rescue agents in dynamic disaster environments, proposes Multiagent-based Artificial Bee Colony (M-ABC) algorithm by improving ABC algorithm without using global information (i.e., local information only), and investigates its effectiveness from the viewpoint of finding victim quickly and efficiently. The intensive simulations on the victim rescue in RoboCup Rescue Simulation System (RCRSS) have revealed the following implications: (1) M-ABC algorithm can rescue victims faster than the full search method as the conventional method. In particular, M-ABC distance (as one of the proposed M-ABC algorithms) can derive the highest performance; (2) M-ABC distance can keep high performance even in dynamical environments where victims move elsewhere; and (3) M-ABC distance can completely rescue victims in dynamical environments, while Ri-one method as the 2012 champion of RoboCup Rescue Simulation League (RCRSL) cannot in such a case.
International conference proceedings, English

環境変化に適応するためのピボット型一般化
佐藤 圭二; 佐藤 寛之; 高玉 圭樹
Last, 計測自動制御学会論文誌, 49, 11, Nov. 2013
Scientific journal, Japanese

複数ロボット協調による大規模構造物組み立てにおける故障ロボット回収タスクの影響
大谷 雅之; 佐藤寛之; 服部 聖彦; 高玉 圭樹
Last, 電気学会論文誌C, The Institute of Electrical Engineers of Japan, 131, 9, 1729-1737, 01 Sep. 2013, This paper focuses on the distributed control of the multiple robots which may be broken and investigates how the robots complete their task by collecting broken robots through the simulation of the large-scale structure assembly. For this purpose, we conduct multiagent simulation for collecting broken robots under the different failure rate of robots. Through the intensive simulations, we have revealed that a collection of broken robots before completing their own task (i.e., deploying their panel) is more effective than after complete their own task.
Scientific journal, Japanese

Analyzing Program Evolution in Genetic Programming using Asynchronous Evaluation
Harada, T; Takadama, K
Last, ECAL2013, 713-720, Sep. 2013
International conference proceedings, English

複数ロボット協調により大規模構造物組み立てにおける故障ロボット回収の影響
大谷雅之; 佐藤寛之; 服部聖彦; 高玉圭樹
Last, 電気学会論文誌, 133, 9, 1729-1727, Sep. 2013
Scientific journal, Japanese

異文化ゲームにおける好意情報の提示とその影響
森 有紗美; 市川 嘉裕; 髙玉 圭樹
人工知能学会全国大会論文集, 一般社団法人 人工知能学会, 2013, 0, 1B34-1B34, 2013,

本研究では，個人の利益が他人や集団全体の利益と競合する社会的問題において，集団を適応状態に導くために効果的な外的要因を探ることを目的とする．その目的に向け，Cialdini の提唱した交渉相手の承認を引き起こす要因となる人間の行動パターンの中から他者からの「好意」に着目し，それが集団内の人間の考え方を変え集団適応状態をもたらすかを異文化体験ゲーム「バルンガ」の被験者実験を通して検証する．

Japanese

Analysis of emission right prices in greenhouse gas emission trading via agent-based model
Tomohiro Nakada; Keiki Takadama; Shigeyoshi Watanabe
Multiagent and Grid Systems, 9, 3, 227-246, 2013, Peer-reviwed, This paper proposes a participant nation model for international emission trading
adaptive agents are used to explore the conditions under which an emission trading market is successful. In this study, the participation nation models with and without the compliance mechanism as prescribed in the Kyoto Protocol are compared
the simulation results for these two cases show a significant difference in both the market price and the amount of gas emissions. Intensive simulations revealed the following successful conditions for the compliance mechanism: (1) the different carbon emission reduction targets for participant nations, as prescribed in the Kyoto Protocol, contributes to reducing the emission amount, (2) there exists a critical boundary for success (i.e., less than -6% emission reduction target) when all participant nations set negative emission reduction targets, (3) a few developing countries with positive emission reduction targets are indispensable for maintaining market trading, and (4) the use of the compliance mechanism helps reduce emissions, while in the case of noncompliance, reduction of emission is not achieved. © 2013 - IOS Press and the authors. All rights reserved.
Scientific journal, English

Asynchronous evaluation based genetic programming: Comparison of asynchronous and synchronous evaluation and its analysis
Tomohiro Harada; Keiki Takadama
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7831, 241-252, 2013, Peer-reviwed, This paper compares an asynchronous evaluation based GP with a synchronous evaluation based GP to investigate the evolution ability of an asynchronous evaluation on the GP domain. As an asynchronous evaluation based GP, this paper focuses on Tierra-based Asynchronous GP we have proposed, which is based on a biological evolution simulator, Tierra. The intensive experiment compares TAGP with simple GP by applying them to a symbolic regression problem, and it is revealed that an asynchronous evaluation based GP has better evolution ability than a synchronous one. © 2013 Springer-Verlag.
International conference proceedings, English

Simple Compact Genetic Algorithm for XCS
Masaya Nakata; Pier Luca Lanzi; Keiki Takadama
2013 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 1718-1723, 2013, This paper proposes a novel rule discovery mechanism for the XCS classifier system, which is an extension of the compact genetic algorithm (cGA) to XCS. Our rule discovery mechanism, like cGA, extracts appropriate attributes of classifier conditions through a probability vector and evolves classifiers using the extracted attributes. Unlike cGA, it newly builds the probability vector at every generations (i.e., it keeps no any probability vectors) not so that it requires XCS to have a lot of probability vectors that represent all available attributes, and mutates classifier conditions based on the extracted attributes as attribute feedback. Experimental results show that XCS with our rule discovery mechanism (or XCScGA) can reach optimal performance with fewer rule evaluations and requires smaller population sizes than XCS. Our conclusion is that the proposed rule discovery mechanism promotes a recombination of building blocks, and that our mutation operator works to repair the classifier conditions towards a compact solutions, hence, XCScGA can generate good offspring which represent maximally general, maximally accurate, and compact solutions.
International conference proceedings, English

Designing internal reward of reinforcement learning agents in multi-step dilemma problem
Yoshihiro Ichikawa; Keiki Takadama
Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, 17, 6, 926-931, 2013, Peer-reviwed, This paper proposes the reinforcement learning agent that estimates internal rewards using external rewards in order to avoid conflict in multi-step dilemma problem. Intensive simulation results have revealed that the agent succeeds in avoiding local convergence and obtains a behavior policy for reaching a higher reward by updating the Q-value using the value that is subtracted the average reward from an external reward.
Scientific journal, English

Characteristic and Application of Network Evolution Model for Public Transport Network
Majima, T; Takadama, K; Watanabe, D; Katsuhara, M
International Transactions on Systems Science and Applications (ITSSA), to appear, 2013, Peer-reviwed
Scientific journal, English

Analyzing Participant Nations in the Greenhouse Gas Emission Trading via Agent-based Model
Nakada, T; Takadama, K; Watanabe, S
International Transactions on Systems Science and Applications (ITSSA), to appear, 2013, Peer-reviwed
Scientific journal, English

Exemplar-based Learning Classifier System: Towards a Generalization of Expert Knowledge
Matsushima, H; Takadama, K
International Transactions on Systems Science and Applications (ITSSA), to appear, 2013, Peer-reviwed
Scientific journal, English

Specifying Collective Adaptive Situations in Cross Cultural Game Towards Validation of Agent-based Social Simulation
Takadama, K; Ushida, Y
SICE Journal of Control, Measurement, and System Integration (JCMSI), The Society of Instrument and Control Engineers, 6, 2, 117-123, 2013, Peer-reviwed, This paper focuses on a collective adaptive situation as an complex phenomena in social dynamics and aims at specifying its situation towards a validation of agent-based social simulations. For this purpose, the fuzzy c-means clustering (FCM) method is applied into the experimental data of both the human subject experiments and the agent-based social simulations in the cross-cultural game, Barnga, and compare these results from the viewpoint of a collective adaptive situation. The analysis of these data has revealed the following implications: (1) the collective adaptive situation can be specified by the FCM method; and (2) the classified results of both of the experimental data are close to each other, indicating that agent-based social simulations can be validated from the viewpoint of a collective adaptive situation.
Scientific journal, English

別カテゴリ商品提示による好みの明確化を促す推薦システム
高玉 圭樹; 佐藤 史盟; 大谷 雅之; 服部 聖彦; 佐藤 寛之; 山口 智弘
人工知能学会論文誌, The Japanese Society for Artificial Intelligence, 28, 2, 210-219, 2013, Peer-reviwed, The paper proposes a novel recommender system which supports users to clarify the most appropriate preference by recommending other categories' items that almost meet the attributes selected by users. Such an advantage is achieved by both the preference ncretization of users and the preference change of users.To investigate the effectiveness of the proposed system, we conducted the human-subject experiments and found that the proposed system supports users to find their desirable items by clarifying their preference. Concretely, the following implications have been revealed: (1) the proposed recommender system with both the serendipity and decision buttons enables users to clarify their preference by comparing items which are classified in different categories; (2) in detail, the item recommendation based on the selected item attributes contributes to clarifying the users' preference through a change of their preference, while the item recommendation based on the item characteristic contributes to clarifying the users' preference through a concretization of their preference; and (3) the proposed recommender system with the decision button succeeds the further clarification of the preference of users who have already clarified it.
Scientific journal, Japanese

A Development of Transportation Simulator for Relief Supply in Disasters
Majima, T; Watanabe, D; Takadama, K; Katsuhara, M
SICE Journal of Control, Measurement and System Integration (JCMSI), 6, 2, 131-136, 2013, Peer-reviwed
Scientific journal, English

The Layout Optimization of Newssite-inserte Advertisements using Two Conflicting Objectives
Sakamoto, M; Takadama, K
International Journal of Computer Information Systems and Industrial Management (IJCISIM), 5, 615-622, 2013, Peer-reviwed
Scientific journal, English

Towards a Care Support System tha Can Guess The Way Aged Persons Feel
Takadama, K
Data Driven Wellness: From Self-Tracking to Behavior Change, AAAI, 45-50, 2013, Peer-reviwed
International conference proceedings, English

Selection strategy for XCS with adaptive action mapping
Masaya Nakata; Pier Luca Lanzi; Keiki Takadama
GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference, 1085-1092, 2013, Peer-reviwed, XCS with Adaptive Action Mapping (XCSAM) evolves solutions focused on classifiers that advocate the best action in every state. Accordingly, XCSAM usually evolves more compact solutions than XCS which, in contrast, works toward solutions representing complete state-action mappings. Experimental results have however shown that, in some problems, XCSAM may produce bigger populations than XCS. In this paper, we extend XCSAM with a novel selection strategy to reduce, even further, the size of the solutions XCSAM produces. The proposed strategy selects the parent classifiers based both on their fitness values (like XCS) and on the effect they have on the adaptive map. We present experimental results showing that XCSAM with the new selection strategy can evolve more compact solutions than XCS which, at the same time, are also maximally general and maximally accurate. Copyright © 2013 ACM.
International conference proceedings, English

Two-stage non-dominated sorting and directed mating for solving problems with multi-objectives and constraints
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference, 647-654, 2013, Peer-reviwed, We propose a novel constrained MOEA introducing a parents selection based on a two-stage non-dominated sorting of solutions and directed mating in the objective space. In the parents selection, first, we classify the entire population into several fronts by non-dominated sorting based on constraint violation values. Then, we re-classify each obtained front by non-dominated sorting based on objective function values, and select the parents population from upper fronts. The two-stage non-dominated sorting leads to find feasible solutions having better objective function values in the evolutionary process of infeasible solutions. Also, in the directed mating, we select a primary parent from the parents population and pick solutions dominating the primary parent from the entire population including infeasible solutions. Then we select a secondary parent from the picked solutions and apply genetic operators. The directed mating utilizes valuable genetic information of infeasible solutions to enhance convergence of each primary parent toward its search direction in the objective space. We compare the search performance of the two proposed algorithms using greedy selection (GS) and tournament selection (TS) in the directed mating with the conventional CNSGA-II and RTS algorithms on SRN, TNK, OSY and m objectives k knapsacks problems. We show that the proposed algorithms achieve higher search performance than CNSGA-II and RTS on all benchmark problems used in this work. Copyright © 2013 ACM.
International conference proceedings, English

Agent-based social simulation to investigate the occurrence of pareto principal in human society
Khan Md Mahfuzus Salam; Takadama Keiki; Nishio Tetsuro
Studies in Computational Intelligence, Springer Verlag, 493, 259-265, 2013, Peer-reviwed, Agent-based simulation is getting more attention in recent days to investigate the social phenomena. This research focused on Pareto principal, which is widely known in the field of Economics. Our motivation is to investigate the reason of why the Pareto principal exists in the human society. We proposed a model for human-agent and conduct simulation. Our simulation result converges to Pareto principal that justifies the effectiveness of our proposed human agent model. Based on the simulation result we found that due to some factors in human character Pareto principal occurred in human society. © Springer International Publishing Switzerland 2013.
Scientific journal, English

Robust Bus Route Optimization to Destruction of Roads
Kitagawa, H; Sato, K; Takadama, K; Sato, H; Hattori, K
5thInternational Workshop on Emergent Intelligence onNetwork Agents (WEIN2013), at 12th International Joint Conference on Autonomous Agents and Multi-agent System(AAMAS2013), 173-185, 2013, Peer-reviwed
International conference proceedings, English

Analysis on the Number of XCS Agents in Agent-based Computational Finance
Tomohiro Nakada; Keiki Takadama
PROCEEDINGS OF THE 2013 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR FINANCIAL ENGINEERING & ECONOMICS (CIFER), IEEE, 8-13, 2013, Peer-reviwed, An agent-based simulation developed as a tool to analyze economic system and social systems since the 1990s. Previous paper reported that the simulation results indicated that the number of agents affects the trading prices and their distributions. To analyze the effect of the number of agents, this paper analyzes the relationship between the number of agents and simulation results using XCS agents for artificial trading. We report the market price fluctuation and population size of internal model by the number of agents. The revealed the following remarkable implications: (1) increasing number of XCS agents does not affect the convergence of population size of all agents; and (2) all agents converge towards approximately form 15 % to 20 % of population size by learning classifier system of XCS agents; and (3) increasing number of XCS agents reduce the variance of the market price.
International conference proceedings, English

Towards understanding of relationship among pareto optimal solutions in multi-dimensional space via interactive system
Keiki Takadama; Yuya Sawadaishi; Tomohiro Harada; Yoshihiro Ichikawa; Keiji Sato; Kiyohiko Hattori; Hiroyoki Sato; Tomohiro Yamaguchi
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8018, 3, 137-146, 2013, This paper proposes the interactive system that can help humans to understand the trade-off relationship of Pareto optimal solutions (e.g., good products from a certain aspect) in multi-dimensional space. For this purpose, the following two methods are proposed from the viewpoint of the number of evaluation criteria which should be considered by a user at one time: (i) the two fixed evaluation criteria are employed to evaluate the solutions
and (ii) some evaluation criteria selected by a user (i.e., the number of the evaluation criteria is varied by a user) are employed to evaluate them. To investigate the effectiveness of our proposed system employing either of two methods, we conduct human subject experiments on the motor selection problem and have revealed the following implications: (i) the proposed system based on the two fixed evaluation criteria contributes to helping users to find better motors in terms of all the evaluation criteria, while (ii) the proposed system based on the selected evaluation criteria is more effective to help users to understand Pareto optimal solutions when more evaluation criteria need to be considered. © 2013 Springer-Verlag Berlin Heidelberg.
International conference proceedings, English

Modeling a human's learning and mastery processes toward learning support system on human computer interaction
Takemori, K; Yamaguchi, T; Sasaji, K; Takadama, K
The 15th International Conference on Human-Computer Interaction(HCI International 2013), Lecture Notes in Computer Science, 8016, 555-564, 2013
International conference proceedings, English

Concierge-based Care Support System for Designing Your Own Lifestyle
Takadama, K
Big Data Becomkes Personal: Knowledge into Meaning, to appear, 2013
International conference proceedings, English

Sleep Stage Estimation Using Synthesized Data of Heart Rate and Body Movement
Tajima, Y; Nakata, M; Sato, K; Takadama,K
Big Data Becomes Personal: Knowledge into Meaning, to appear, 2013
International conference proceedings, English

Estimation of Cooperation Navigation by Multiple Rovers Using Different Positioning Technique
Homma, E; Kagawa, T; Hattori, K; Otani, M; Nakata, M; Ichikawa, Y; Harada, T; Matsushima, H; Sato, K; Takadama, K; Nakajima, N
The 29th International Symposium on Space Technology and Science: ISTS 2013, 2013, 6, 2013, Peer-reviwed
International conference proceedings, English

Sleep stage estimation by evolutionary computation using heartbeat data and body-movement
Hiroyasu Matsushima; Kazuyuki Hirose; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
International Journal of Advancements in Computing Technology, 4, 22, 281-290, Dec. 2012, Peer-reviwed, This paper focuses on distinctive changes of not only the heart rate but also the body movement in REM stage (i.e., light sleep) and Non-REM stage (i.e., deep sleep) and improves our sleep estimation method by employing the feature of such distinctive changes. In particular, the heart rate increases irregularly in REM stage, while the heart rate decreases in Non-REM stage. The body moves intensively in REM stage, while the body does not frequently move in Non-REM stage. Using such distinctive changes, we propose a new fitness function which determines the REM/Non-REM stage and introduce it into for our sleep estimation method based on Genetic Algorithms (GAs), which evolve the sleep stage for each person according to the fitness. To investigate an effectiveness of a new fitness function, we compare the estimated sleep stages of our method employing the proposed fitness function with that of Watanabe's method as the conventional method. The experimental results suggest that our method employing the proposed fitness function has a capability to estimate the sleep stage accurately than Watanabe's method without connecting any devices.
Scientific journal, English

Evolutionary optimization for feeder route network using multi-objective clustering
Keiji Sato; Saori Iseya; Keiki Takadama; Hiroyuki Sato; Kiyohiko Hattori
International Journal of Advancements in Computing Technology, 4, 22, 269-280, Dec. 2012, Peer-reviwed, This paper extends the demand priority-based Genetic Algorithm (namely the demand pri-GA) by employing the cluster-first-route-second method to generate the effective feeder route network consisted of the hub-spoke and loop type networks. For this purpose, we propose the feeder route network optimization method which integrates the demand pri-GA with the multi-objective clustering in order not to fall into local minima of the feeder route network. Through intensive simulations on the Busan-centered feeder route network optimization problem, we have revealed that (1) the proposed method contributes to avoiding to fall into local minima such as the feeder route network which including the high cost routes
and (2) a relaxation of cluster restrictions in the proposed method contributes to generating a various low cost feeder routes network.
Scientific journal, English

学習進度に基づくマルチエージェントQ学習における競合回避
市川 嘉裕; 高玉 圭樹
Last, 計測自動制御学会論文誌, 48, 11, Nov. 2012, Peer-reviwed
Research institution, Japanese

予測報酬に基づく個別化による学習分類子システムの学習性能の向上
中田 雅也; 原田 智広; 佐藤 圭二; 松島 裕康; 高玉 圭樹
Last, 計測自動制御学会論文誌, 計測自動制御学会 ; [196-]-, 48, 11, 713-722, Jun. 2012, Peer-reviwed
Scientific journal, Japanese

超大規模センサネットワークに適応したセンサデータ収集・分散故障診断アルゴリズム
服部 聖彦; 高玉 圭樹; 大谷 雅之; 松島 裕康; 佐藤 圭二; 市川 嘉裕
計測自動制御学会論文誌, 計測自動制御学会 ; [196-]-, 48, 11, 745-753, May 2012
Scientific journal, Japanese

XCS with Adaptive Action Mapping.
Masaya Nakata; Pier Luca Lanzi; Keiki Takadama
Simulated Evolution and Learning - 9th International Conference, SEAL 2012, Hanoi, Vietnam, December 16-19, 2012. Proceedings, Springer, 138-147, 2012, Peer-reviwed

Enhancing Learning Capabilities by XCS with Best Action Mapping.
Masaya Nakata; Pier Luca Lanzi; Keiki Takadama
Parallel Problem Solving from Nature - PPSN XII - 12th International Conference, Taormina, Italy, September 1-5, 2012, Proceedings, Part I, Springer, 256-265, 2012, Peer-reviwed

Computational Time Reduction of Evolutionary Spacecraft Location Estimation toward Smart Lander for Investigating Moon
Harada, T; Usami, R; Takadama, K; Kamata, H; Ozawa, S; Fukuda, S; Sawai, S
The 11th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS2012), 10C--4, 2012, Peer-reviwed
International conference proceedings, English

Age-based Sleep Stage Estimation by Evolutionary Algorithm,
Matsushima, H; Minami, S; Takadama, K
The AAAI 2012 Spring Symposia, AAAI (The Association for the Advancement of Artificial Intelligence), 42--47, 2012, Peer-reviwed
International conference proceedings, English

Exploring Individual Care Plan for a Good Sleep
Takadama, K
The AAAI 2012 Spring Symposia, AAAI (The Association for the Advancement of Artificial Intelligence),, 60--64, 2012, Peer-reviwed
International conference proceedings, English

Preference Clarification Recommender System by Searching Items Beyond Category
Takadama; K. Sato, F; Otani, M; Hattori, K; Sato, H; Yamaguchi, T
The IADIS Interfaces and Human Computer Interaction 2012 Conference (IHCI 2012), 3-10, 2012
International conference proceedings, English

Evolutionary Algorithm for Uncertain evaluation function
Tajima, Y; Nakata, M; Takadama, K
The 16th International Symposium on Intelligent and Evolutionary Systems (IES 2012), 40-45, 2012, Peer-reviwed
International conference proceedings, English

Towards Network Optimization of Regular and Non-regular Flights
Jimba, T; Kitagawa, H; Azuma, E; Sato, K; Sato, H; Hattori, K; Takadama, K
The 16th International Symposium on Intelligent and Evolutionary Systems (IES 2012), 124-128, 2012, Peer-reviwed
International conference proceedings, English

Designing Internal Reward of Reinforcement Learning Agents for Conflict Avoidance in Multi-step Dilemma Problem
Ichikawa, Y; Takadama, K
The 16th International Symposium on Intelligent and Evolutionary Systems (IES 2012), 152-157, 2012, Peer-reviwed
International conference proceedings, English

Evolving Conditional Branch Programs in Tierra-based Asynchronous Genetic Programming
Tomohiro Harada; Yoshihiro Ichikawa; Keiki Takadama
6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS, IEEE, 1023-1028, 2012, Peer-reviwed, This paper explores the methods which can evolve conditional branch programs in Tierra-based Asynchronous Genetic Programming (TAGP) to improve an evolutionary ability for complex programs. For this purpose, we propose three methods, namely, the label address, the elite preserving strategy with the program size restriction, and the gradient fitness calculation. An intensive experiment on a calculation program evolution reveals the following implications: (1) the label addressing can simply construct the conditional branch; (2) the elite preserving strategy contributes to maintaining the correct programs and the program size restriction prevents the ineffective instructions; and (3) the gradient fitness calculation can correctly evaluate the multiple outputs programs; and (4) the above three methods, however, are difficult to generate the shortest size programs such as sharing instructions with different calculations.
International conference proceedings, English

Interactive Assistant System for Understanding Pareto solutions in Multi-Dimensional Space
Sawadaishi, Y; Harada, T; Ichikawa, Y; Takadama, K
Triangle Symposium on Advanced ICT 2012 (TriSAI 2012), SJ16, 2012, Peer-reviwed
International conference proceedings, English

Computational Time Reduction of Evolutionary Spacecraft Location Estimation toward Smart Lander for Investigating Moon
Harada, T; Usami, R; Takadama, K; Kamata, H; Ozawa, S; Fukuda, S; Sawai, S
The 11th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS2012), 10C-04, 10C--4, 2012, Peer-reviwed
International conference proceedings, English

Robust Bus Route Optimization by Connecting/Extending Routes
Kitagawa, H; Sato, K; Sato, H; Hattori, K; Takadama, K
International Workshop on Modern Science and Technology (IWMST 2012), 390-393, 2012, Peer-reviwed
International conference proceedings, English

Optimizing Ship Routes by Evolutionary Computation in Competitive Ship Companies
Azuma, E; Sato, K; Sato, H; Hattori, K; Takadama, K
International Workshop on Modern Science and Technology (IWMST 2012), 378-383, 2012, Peer-reviwed
International conference proceedings, English

Layout optimization of Advertisements on News Websites by Genetic Algorithm
Muramatsu, N; Takadama, K; Sato, H; Sakamoto, M
International Workshop on Modern Science and Technology (IWMST 2012), 384-389, 2012
International conference proceedings, English

Awareness based recommendation - Toward the Cooperative Learning in Human Agent Interaction -
Yamaguchi, T; Nishimura, T; Takadama, K
International Conference on Humanized System 2012 (ICHS 2012), 198-203, 2012
International conference proceedings, English

Bridging local and global: multi-agent reinforcement learning from function approximation perspective
Wada, A; Takadama, K; Shimohara, K
International Conference on Humanized System 2012 (ICHS 2012), 204-208, 2012
International conference proceedings, English

Automatic Cartography Method by Cooperation of Autonomous Micro Robots and Its Verification Through Simulations
Hattori, K; Homma, E; Otani, M; Ichikawa. Y; Matsushima, H; Sato, K; Harada, T; Takadama, K
International Conference on Humanized System 2012 (ICHS 2012), 228-231, 2012, Peer-reviwed
International conference proceedings, English

Cluster-based Bus Route Optimization for Stranded Persons in Disaster
Kitagawa, H; Sato, K; Sato, H; Hattori, K; Takadama, K
International Conference on Humanized System 2012 (ICHS 2012), 224-227, 2012, Peer-reviwed
International conference proceedings, English

Ship Route Optimization by Multi-Objective Evolutionary Computation based on Dynamic Reference Point
Azuma, E; Sato, K; Sato, H; Hattori, K; Takadama, K
International Conference on Humanized System 2012 (ICHS 2012), 209-214, 2012, Peer-reviwed
International conference proceedings, English

Entropy-based Conflict Avoidance According to Learning Progress in Multi-Agent Q-learning
Ichikawa, Y; Sato, K; Hattori, K; Takadama, K
The IADIS International Conference Intelligent Systems and Agents 2012 (ISA 2012), 20-30, 2012, Peer-reviwed
International conference proceedings, English

Age-based Sleep Stage Estimation by Evolutionary Algorithm,
Matsushima, H; Minami, S; Takadama, K
The AAAI 2012 Spring Symposia, AAAI (The Association for the Advancement of Artificial Intelligence), 42--47-47, 2012, Peer-reviwed
International conference proceedings, English

Crater Detection using Haar-Like Feature for Moon landing System Based on the Surface Image
Tanaami, T; Takeda, Y; Aoyama, N; Mizumi, S; Kamata, N; Takadama, K; Ozawa, S; Fukuda, S; Sawai, S
The 28th International Symposium on Space Technology and Science (ISTS2011), 10, ists28, 2011-d-31, Jun. 2011, Peer-reviwed
International conference proceedings, English

Crater Detection using Haar-Like Feature for Moon landing System Based on the Surface Image
Tanaami, T; Takeda, Y; Aoyama, N; Mizumi, S; Kamata, N; Takadama, K; Ozawa, S; Fukuda, S; Sawai, S
The 28th International Symposium on Space Technology and Science (ISTS2011), 2011-d-37, ists28, 2011-d-31, Jun. 2011, Peer-reviwed
International conference proceedings, English

Special issue on new trends in agent-based simulation
Keiki Takadama; Kiyoshi Izumi
Journal of Advanced Computational Intelligence and Intelligent Informatics, 15, 2, 165, Mar. 2011
Scientific journal

個別化による学習分類子システムの一般化促進
中田 雅也; 原田 智広; 佐藤 圭二; 松島 裕康; 高玉圭樹
Last, 計測自動制御学会論文誌, 47, 11, 581-590, Mar. 2011, Peer-reviwed
Scientific journal, Japanese

Bayesian Analysis Method of Time Series Data in Greenhouse Gas Emissions Trading Market
Tomohiro Nakada; Keiki Takadama; Shigeyoshi Watanabe
AGENT-BASED APPROACHES IN ECONOMIC AND SOCIAL COMPLEX SYSTEMS VI, WORLD SCIENTIFIC PUBL CO PTE LTD, 8, 147-159, 2011, Peer-reviwed, This paper proposes the Bayesian analysis method (BAM) to classify the time series data which derives the complicated phenomena in the international greenhouse gas emissions trading. Our investigation compared the results using the method of Discrete Fourier transform (DFT) and BAM. Such comparisons have revealed the following implications: (1) BAM is superior to DFT in terms of classifying time series data by the different distances; and (2) the different distances in BAM show the importance of 1% influence of emission reduction targets.
International conference proceedings, English

What Kinds of Human Negotiation Skill Can Be Acquired by Changing Negotiation Order of Bargaining Agents?
Keiki Takadama; Atsushi Otaki; Keiji Sato; Hiroyasu Matsushima; Masayuki Otani; Yoshihiro Ichikawa; Kiyohiko Hattori; Hiroyuki Sato
Human Interface and the Management of Information. Interacting with Information - Symposium on Human Interface 2011, Held as Part of HCI International 2011, Orlando, FL, USA, July 9-14, 2011, Proceedings, Part II, Springer, 335-344, 2011, Peer-reviwed

Pittsburgh-style learning classifier system for multiple environments: towards robust waterbus route for several situations.
Keiji Sato; Keiki Takadama
IJBIC, 3, 6, 370-383, 2011, Peer-reviwed

Decision-making Model Using XCS in Artificial Market
Nakada, T; Takadama, K; Watanabe, S
Workshop on Agent-Based Computational Economics and Finance, 1--5, 2011, Peer-reviwed
International conference proceedings, English

The proposal of the planetary exploration system with the cooperation and sequential movement of two micro rovers
Hattori, K; Nakata, M; Ichikawa, Y; Otani, M; Matsushima, H; Takadama, K
The 28th International Symposium on Space Technology and Science, 2011-d-31, 2011, Peer-reviwed
International conference proceedings, English

Optimal Positions of Advertisements on News Websites Focusing on Three Conflicting Objectives
Muramatsu, N; Takadama, K; Sakamoto, M
The IADIS Interfaces and Human Computer Interaction 2011 Conference ( ISTS 2011), 394--398, 2011, Peer-reviwed
International conference proceedings, English

Classification of Collective Adaptive Situations in Cross Cultural Game by Fuzzy C-Means Clustering
Ushida, Y; Takadama, K
SICE Annual Conference 2011, 1224--1229, 2011, Peer-reviwed
International conference proceedings, English

`Agent-based Modeling Using XCS in Greenhouse Gas Emissions Trading Market
Nakada, T; Takadama, K
SICE Annual Conference 2011, 519--523, 2011, Peer-reviwed
International conference proceedings, English

Support System for Transportation under Disaster Circumstance
Majima., T; Watanabe, D; Takadama, K; Katsuhara, M
SICE Annual Conference 2011, 137-142, 2011, Peer-reviwed
International conference proceedings, English

Towards generalization by identification-based XCS in multi-steps problem
Masaya Nakata; Fumiaki Sato; Keiki Takadama
Proceedings of the 2011 3rd World Congress on Nature and Biologically Inspired Computing, NaBIC 2011, 389-394, 2011, Peer-reviwed, This paper extends an accuracy-based Learning Classifier System (XCS) to promote a generalization of classifiers by selecting effective ones and deleting ineffective ones, and calls it Identification-based XCS (IXCS). Through the intensive simulations of the Maze problem (Maze6), the following implications have been revealed : (1) IXCS can derive good solutions with a fewer number of classifiers in comparison with XCSG as one of the major conventional XCS
and (2) IXCS can not only generalize the classifiers faster but also generate the classifiers that are robust to the noisy environment. © 2011 IEEE.
International conference proceedings, English

Adaptive mutation depending on program size in asynchronous program evolution
Tomohiro Harada; Keiki Takadama
Proceedings of the 2011 3rd World Congress on Nature and Biologically Inspired Computing, NaBIC 2011, 433-438, 2011, Peer-reviwed, This paper proposes an adaptive mutation method which changes a mutation rate depending on the program size in the asynchronous program evolution unlike the synchronous program evolution such as genetic programming. An intensive experiment with an evolution of calculation programs has revealed that the proposed adaptive mutation method can generate the correct and short programs in comparison with other methods. © 2011 IEEE.
International conference proceedings, English

Multi-objective Constrained Optimization Evolutionary Algorithm by Concurrent Solution Search in Feasible and Infeasible Area,
Shimada, T; Matsushima, H; Takadama, K
The 15th International Symposium on Intelligent and Evolutionary Systems (IES 2011), 111--116, 2011, Peer-reviwed
International conference proceedings, English

Sleep Stage Estimation By Evolutionary Computation Using Heartbeat Data and Body-Movement
Matsushima, H; Hirose, K; Hattori, K; Sato, H; Takadama, K
The 15th International Symposium on Intelligent and Evolutionary Systems (IES 2011), 103--110, 2011, Peer-reviwed
International conference proceedings, English

Evolutionary Optimization for Feeder Route Network using Multi-Objective Clustering,
Sato, K; Iseya, S; Takadama, K; Sato, H; Hattori, K
The 15th International Symposium on Intelligent and Evolutionary Systems (IES 2011),, 76--83, 2011, Peer-reviwed
International conference proceedings, English

The biased multi-objective optimization using the reference point: Toward the industrial logistics network
Eriko Azuma; Tomohiro Shimada; Keiki Takadama; Hiroyuki Sato; Kiyohiko Hattori
Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011, 2, 27-30, 2011, Peer-reviwed, This paper explores the multi-objective evolutionary algorithm that can effectively solve a multi-objective problem where an importance of the objective differs each other unlike the conventional problem which concerns each objective evenly. Since such a type of a problem is often found in industrial problems (e.g., logistics network), we propose the biased multi-objective optimization using the reference point (i.e., the factor of strongly concerned). Intensive experiment on the multi-objective knapsack problem had revealed that our proposed method was more superior and had higher diversity than the conventional multi-objective optimization method. © 2011 IEEE.
International conference proceedings, English

Cooperation among multiple robots by collecting broken robots in large-scale structure assembly
Masayuki Otani; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
2011 IEEE/SICE International Symposium on System Integration, SII 2011, 691-696, 2011, Peer-reviwed, This paper focuses on the distributed control of the multiple robots which may be broken and investigates how the robots complete their task by collecting broken robots through the large-scale structure assembly. For this purpose, we conduct simulations of our proposed deadlock avoidance method with collecting broken robots under the different failure rate of robots. Through the intensive simulations, we have revealed that (1) our deadlock avoidance method with collecting broken robots completes assembly faster and more certainly than the method without collecting broken robots
and (2) a collection of broken robots before completing their own task (Le., deploying their panel) is more effective than after complete their own task. © 2011 IEEE.
International conference proceedings, English

Robustness to bit inversion in registers and acceleration of program evolution in on-board computer
Tomohiro Harada; Masayuki Otani; Yoshihiro Ichikawa; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, 15, 8, 1175-1185, 2011, Peer-reviwed, This paper focuses on an on-board computer (OBC) that evolves computer programs through bit inversion and targets analyzing robustness against bit inversion in registers. We also propose a new method that can change the number of computer programs dynamically. Intensive experiments revealed the following: (1) Correct programs can be maintained even in bit inversion in registers in addition to bit inversion in instructions. (2) Our proposal accelerates program evolution by increasing the population size, i.e., the number of programs, within fixed memory size.
Scientific journal, English

Modeling collective adaptive agent design and its analysis in Barnga game
Yuya Ushida; Kiyohiko Hattori; Keiki Takadama
JOURNAL OF ECONOMIC INTERACTION AND COORDINATION, SPRINGER HEIDELBERG, 5, 2, 137-154, Dec. 2010, Peer-reviwed, This paper explores the collective adaptive agent that adapts to a group in contrast with the individual adaptive agent that adapts to a single user. For this purpose, this paper starts by defining the collective adaptive situation through an analysis of the subject experiments in the playing card game, Barnga, and investigates the factors that lead the group to the collective adaptive situation. Intensive simulations using Barnga agents have revealed the following implications: (1) the leader who takes account of other players' opinions contributes to guide players to the collective adaptation situation, and (2) an appropriate role balance among players (i.e., the leader, the claiming and quiet players, which make the most and least number of corrections of the leader's decision) is required to derive the collective adaptive situation.
Scientific journal, English

Anaysis of Air Cargo Hub Location in East Asia
Watanabe, D; Majima, T; Takadama, K; Katsuhara, M
The Third International Conference on Transportation and Logistics (T-LOG2010) (CDROM) (2010), CDROM, Dec. 2010, Peer-reviwed
International conference proceedings, English

Investigation of Elements for Leadership By Hybrid Intelligent Systems
Ushida, Y; Takadama, K; Zhang, M
The IADIS International Conference on Internet Technologies Society (ITS2010),, 83-90, Nov. 2010, Peer-reviwed
International conference proceedings, English

Pittsburgh-style Learning Classifier System for Multiple Environments: Towards Robust Waterbus Route for Several Situations
Sato, K; Takadama, K
The 14th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES2010), 61-68, Nov. 2010, Peer-reviwed
International conference proceedings, English

Conflict Avoidance using Information Entropy in Multi-Agent Learning Environment
Ichikawa, Y; Hattori, K; Takadama, K
The 14th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES2010), 78-84, Nov. 2010, Peer-reviwed
International conference proceedings, English

Hybrid Directional-Biased Evolutionary Algorithm for Multi-Objective Optimization
Tomohiro Shimada; Masayuki Otani; Hiroyasu Matsushima; Hiroyuki Sato; Kiyohiko Hattori; Keiki Takadama
Parallel Problem Solving from Nature PPSN XI, 121-130, Sep. 2010, Peer-reviwed
International conference proceedings, English

燃料価格の変動による国内航空貨物運賃への影響とその特性に関する国際比較
渡部大輔; 間島隆博; 高玉圭樹; 勝原光治郎
日本物流学会誌, 18, 145-152, 31 May 2010
Japanese

Learning Multiple Band-Pass Filters for Sleep Stage Estimation: Towards Care Support for Aged Persons
Keiki Takadama; Kazuyuki Hirose; Hiroyasu Matsushima; Kiyohiko Hattori; Nobuo Nakajima
IEICE TRANSACTIONS ON COMMUNICATIONS, IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG, E93B, 4, 811-818, Apr. 2010, Peer-reviwed, This paper proposes the sleep stage estimation method that can provide an a;:curate estimation tiff each person without connecting any devices to human's body In particular, our method learns the appropriate multiple band-pass filters to extract the specific wave pattern of heartbeat. which Is required to estimate the sleep stage For an accurate estimation. this paper employs Learning Classifier System (LCS) as the data-mining techniques and extends it to estimate the sleep maize Extensive experiments on five subjects in mixed health confirm the following implications (1) the proposed method can provide more accurate sleep Stage estimation than the conventional method. and (2) the sleep sine estimation calculated by the proposed method is robust regardless of the physical condition of the subject
Scientific journal, English

マルチエージェントシステムによる路線網構築法
間島 隆博; 高玉 圭樹
Last, オペレーションリサーチ学会論文誌, 55, 3, 170-175, Mar. 2010
Japanese

エージェントベースシミュレーションを用いた国際排出権取引市場における時系列データの分類法の提案
仲田 知弘; 高玉 圭樹; 渡辺 成良
計測自動制御学会論文集, 46, 9, 2010, Peer-reviwed
Scientific journal, Japanese

Toward strategic human skill development through human and agent interaction: Improving negotiation skill by interacting with bargaining agent
Atsushi Otaki; Kiyohiko Hattori; Keiki Takadama
Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, 14, 7, 831-839, 2010, Peer-reviwed, This paper focuses on developing human skills through interaction between a human player and a computer agent, and explores its strategic method through experiments on the bargaining games where human players negotiate with computer agents. Specifically, human players negotiate with three types of agents: (a) strong/weak attitude agents making aggressive/defensive proposals in advantageous/disadvantageous situations
(b) fair agents making fair proposals
and (c) the "human-like" agents making mutually agreeable proposals as the number of games increases. Analysis of the human subject experiments has revealed the three major implications: (1) human players negotiating with the strong/weak attitude agents obtain the largest profit overall
(2) human players negotiating with "human-like" agents win many games
and (3) no relationship exists between profit maximization and a win of the games.
Scientific journal, English

Evolutionary optimization for hub and spoke network based on demand and operation
Saori Iseya; Keiki Takadama
Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010, 678-683, 2010, Peer-reviwed, This paper proposes the optimization method which extends Priority-based Genetic Algorithm (Pri-GA) to generate the effective feeder route which has hub-spoke and loop type network. In detail, the following extensions are introduced: (1) the route connection base on port demands, and (2) ship size changing based on operation form. To investigate the effectiveness of the proposed methods, this paper applies them into Pri-GA to optimize the Busan centered feeder route using the actual transportation data. The simulation have revealed following indications: (1) the generated routes using proposed methods contribute to reduce the moving and the shipping cost, and (2) our proposed methods contribute to allocate adequate ships with considering the port demands. © 2010 IEEE.
International conference proceedings, English

Improving Sleep Stage Estimation by Specializing Multiple Band-Pass Filters and Discrete Heartbeat Data,
Takadama, K; Hirose, K; Matsushima, H; Hattori, K; Sato, H; Nakajima, N
The Fourth International Symposium on Medical Information and Communication Technology (ISMICT 2010), *, *, 2010, Peer-reviwed
International conference proceedings, English

Towards Spiral Care Support System: Evaluating Sleep Stage for Care Plan Optimization
Takadama, K
The Fourth International Symposium on Medical Information and Communication Technology (ISMICT 2010), *, *, 2010, Peer-reviwed
International conference proceedings, English

Robustness of deadlock avoidance in assembling large-scale structure by multiple robots having trouble and individual differences
Otani, M; Hattori, K; Sato, H; Takadama, K
The 18th IFAC Symposium on Automatic Control in Aerospace (ACA 2010), *, *, 391-396, 2010, Peer-reviwed
International conference proceedings, English

Single Hub Location Model of Air Cargo in East Asia,
Watanabe, D; Majima, T; Takadama, K; Katsuhara, M
The 3rd International Conference on Transportation and Logistics(T-LOG 2010), *, *, 2010, Peer-reviwed
International conference proceedings, English

Improving Recovery Capability of Multiple Robots in Different Scale Structure Assembly,
Otani, M; Hattori, K; Sato, H; Takadama, K
The 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS2010), *, *, 1186-1196, 2010, Peer-reviwed
International conference proceedings, English

The Layout Optimization of Newssite-inserted Advertisements Using Two Conflicting Objectives,
Muraoka, K; Takadama, K; Sakamoto, M
The IADIS Interfaces and Human Computer Interaction 2010 Conference (IHCI 2010), *, *, 2010, Peer-reviwed
International conference proceedings, English

Serendipity-based Recommender System in Web Shopping,
Sato, F; Otaki, A; Hattori, K; Sato, H; Takadama, K; Yamaguchi, T
The IADIS Interfaces and Human Computer Interaction 2010 Conference (IHCI 2010), *, *, 2010, Peer-reviwed
International conference proceedings, English

Large-scale structure assembly by multiple robots which may be broken
Masayuki Otani; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
Lecture Notes in Electrical Engineering, 67, *, 567-576, 2010, Peer-reviwed, This paper investigates how to design the limit of failure rate and the adjust number of robots in the distribution control of the multiple robots which may be broken through the simulation of space solar power satellite assembly. For this purpose, we conduct simulations with changing the failure rate of robots that employ our proposed deadlock avoidance method. Intensive simulations have revealed the following implications: (1) from the viewpoint of the completion rate, our deadlock avoidance method enables the robots to complete the assembly in 80% completion rate even if the 1/3 robots are broken
(2) from the viewpoint of the recovery rate (i.e., the rate of completing a task when some of robots are broken), the maximum failure rate which enables robots to complete the assembly in 80% is 0.2%, i.e., the 1/3 robots can be broken
and (3) when the failure rate is 0.2%, it is possible to maximize the completion rate from 80% to 90% by adjust the number of the robots. © 2010 Springer-Verlag Berlin Heidelberg.
International conference proceedings, English

Fly to Venus: Program Evolving On-Board Computer in UNITEC-1
Takadama, K; Harada, T; Nakazawa, K; Otani, M; Matsushima,H; Hattori, K
The Third International Symposium on Robot and Artificial Intelligence, *, *, 2010, Peer-reviwed
International conference proceedings, English

The Multi-Agent Technique Focusing on Learning Progress
Ichikawa, Y; Matsushima, H; Hattori, K; Takadama, K
The 3rd International Symposium on Knowledge Acquisition and Modeling, *, *, 2010, Peer-reviwed
International conference proceedings, English

Evolving Complex Programs in Tierra-Based On-Board Computer on UNITEC-1
Harada, T; Otani, M; Matsushima, H; Hattori, K; Takadama, K
The 61th International Astronautical Congress, *, *, 2010, Peer-reviwed
International conference proceedings, English

Adaptive Agent Design in Highly-Dynamic Environment via Barnga Game,
Ushida, Y; Hattori, K; Takadama, K
The Third World Congress on Social Simulation, *, *, 2010, Peer-reviwed
International conference proceedings, English

Classification of Price Fluctuation using Bayesian Analysis Method in U-Mart
Nakada, T; Takadama, K; Watanabe, S
The Third World Congress on Social Simulation, *, *, 2010, Peer-reviwed
International conference proceedings, English

Dynamic matching range in Exemplar-based Learning Classifier System
Hiroyasu Matsushima; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, *, *, 2010, Peer-reviwed, This paper proposes the extended version of Exemplar-based Learning Classifier System (ECS) called DMR-ECS which introduces the basis function for the dynamic matching selection in ECS. In comparision with our previous match selection in ECS, the proposed dynamic match selection in DMR-ECS can control an appropriate range of the match selection automatically to extract the exemplars that cover given problem space. Intensive simulation on the cargo layout problem has revealed that DMR-ECS contributes to not only improving the performance but also reducing the number of the exemplars with an appropriate range of the match selection.
International conference proceedings, English

`Towards Care Plans of Aged Persons by Multi-objective Optimization
Shimada, T; Matsushima, H; Sato, H; Hattori, K; Takadama, K
SICE Annual Conference 2010, SB15, 5, 3258-3263, 2010, Peer-reviwed
International conference proceedings, English

Towards an Objective Generation As an Autonomous Agent Architecture,
Kanamaru; A. Hattori; K. Sato, H; Takadama, K
SICE Annual Conference 2010, SA15, 1, 2763-2768, 2010, Peer-reviwed
International conference proceedings, English

`Proposal of Highly Accurate Position Estimation System Using Movement History of Wireless Terminals and Wireless Communication
Hattori; Nakajima, N; and; Takadama, K
SICE Annual Conference 2010, SA15, 1, 2783-2787, 2010, Peer-reviwed
International conference proceedings, English

Education Difference on Symbol Acquisition Between Educator Agent and Learner Agent
Inoue, T; Matsushima, H; Hattori, K; Takadama, K
International Conference on Humanized Systems 2010 (ICHS2010), 5-9, 2010, Peer-reviwed
International conference proceedings, English

Strategic Human Skill Development by Agent-based Participatory Approach: Improving Negotiation Skill by Interacting with Bargaining Agent,
Otaki, A; Hattori, K; Takadama, K
2009 International Conference on Enterprise Information Systems and Web Technologies (EISWT-09), 244-250, Jul. 2009, Peer-reviwed
International conference proceedings, English

Deadlock avoidance by task transfer among multiple robots in large-scale structure assembly
Otani, M; Hattori, K; Takadama, K
The 27th International Symposium on Space Technology and Science:(ISTS'09), *, *, Jul. 2009, Peer-reviwed
International conference proceedings, English

Network Evolution, Modifying Model for Public Transit Network
間島隆博; 高玉圭樹; 渡部大輔; 勝原光治郎
情報処理学会論文誌, VOL.2.NO.2,92-102, 15 Apr. 2009
Japanese

Time Horizon Generalization in Reinforcement Learning: Generalizing Multiple Q-tables in Q-learning Agents
Hatcho, Y; Hattori, K; Takadama, K
Journal of Advanced Computational Intelligence and Intelligent Informatics, 13, 6, 667-674, 2009, Peer-reviwed
Scientific journal, English

Exemplar Generalization in Reinforcement Learning: Improving Performance with Fewer Exemplars
Matsushima, H; Hattori, K; Takadama, K
Journal of Advanced Computational Intelligence and Intelligent Informatics, 13, 6, 683-690, 2009, Peer-reviwed
Scientific journal, English

Is Gradient Descent Update Consistent with Accuracy-based Learning Classifier System?
Wada, A; Takadama, K
Journal of Advanced Computational Intelligence and Intelligent Informatics, 13, 6, 2009, Peer-reviwed
Scientific journal, English

Analyzing Strenght-based Classifier System from Reinforcement Learning Perspective
Wada, A; Takadama, K
Journal of Advanced Computational Intelligence and Intelligent Informatics, 13, 6, 631-639, 2009, Peer-reviwed
Scientific journal, English

ネットワーク成長，修正モデルによる公共交通機関の路線網構築法
間島 隆博; 高玉 圭樹; 渡部大輔; 勝原光治郎
情報処理学会学会誌, 情報処理学会, 2, 2, 92--102-102, 2009, 自家用車から公共交通機関へのモーダルシフトは，大気汚染の緩和，二酸化炭素排出量の削減に対して大きな効果をもたらすため，LRT （Light Rail Transit） の導入や河川舟運の活用といった検討がさかんに行われている．通常，公共交通機関の輸送システムでは設定された路線内を輸送機材 （バス，船など） が往復する運行形態が採用されるが，路線経路の決定や複数の路線で構成するネットワークの構築は輸送システムのパフォーマンスを左右する重要な問題となる．本稿では，路線で構成されるネットワークを生成するモデルとして，複雑ネットワークの成長モデルにより，初期路線の集合を生成し，生成された路線をエージェントと見立てたマルチエージェントシステムにより，路線網を進化させるモデルを構築した．マルチエージェントシステムは進化ルールを変更，追加することで，様々な路線網を生成できる柔軟なシステムになる可能性が期待され採用した手法であるが，ここでは最適化の側面に焦点を当て報告する．なお，ここで構築したモデルを過去に行われたベンチマーク問題に応用した結果，既存の結果より良い路線網を生成することが確認できた．Since modal shift from automobile to public transport is expected to mitigate air pollution and reduce the emission amount of carbon dioxide, introduction of LRT (Light Rail Transit) or transportation system with rivers is investigated actively. The public transit network is comprised of lines and the organization of the lines affect to the performance of the transportation system. In this paper, a model organizing lines and generating public transit network is proposed. This model has two stages. At first stage, modified network evolution model in the research field of complex network produces the initial line set. At second stage, the lines generated in the first stage evolve as agent in the framework of the multi agent system. The proposed model provided better solutions than that of precedent work for a benchmark problem.
Scientific journal, Japanese

燃料高騰によるトラック運賃への影響とその特性に関する国際比較
渡部 大輔; 間島 隆博; 高玉 圭樹; 勝原 光治郎
日本物流学会誌, 17, 17, 105--112, 2009, Peer-reviwed
Scientific journal, Japanese

Is Gradient Descent Update Consistent with Accuracy-based Learning Classifier System?
Wada, A; Takadama, K
Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), 13, 6, 640-648, 2009, Peer-reviwed
Scientific journal, English

From My Agent to Our Agent: Exploring Collective Adaptive Agent via Barnga
Yuya Ushida; Kiyohiko Hattori; Keiki Takadama
AI 2009: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, SPRINGER-VERLAG BERLIN, 5866, 41-51, 2009, Peer-reviwed, This paper explores the collective adaptive agent that adapts to agroup in contrast with the individual adaptive agent that adapts to a single user. For this purpose, this paper starts by defining the collective adaptive situation through an analysis of the subject experiments in the playing card game, Barnga, and investigates the factors that lead the group to the collective adaptive situation. intensive simulations using Barnga agents have revealed the following implications: (1) the leader who takes account of other players' opinions contributes to guide players to the collective adaptation situation, and (2) an appropriate role balance among players (i.e., the leader, the claiming and quiet player, which make the most and least number of corrections) is required to derive the collective adaptive situation.
International conference proceedings, English

Analysis Method Depending on Bayes's Theorem for Agent-Based Simulations,
Nakada, T; Takadama, K; Watanabe, S
The 6th International Workshop on Agent-based Approaches in Economic and Social Complex Systems (AESCS'09), *, 306-317, 2009, Peer-reviwed
International conference proceedings, English

Generalized Weber Model for Hub Location of Air Cargo
Daisuke Watanabe; Takahiro Majima; Keiki Takadama; Mitujiro Katuhara
OPERATIONS RESEARCH AND ITS APPLICATIONS, PROCEEDINGS, WORLD PUBLISHING CORPORATION, 10, 124-+, 2009, Peer-reviwed, Many of air cargo companies adapt the hub-and-spokes system where economies of scale exist in the transportation cost. In this paper, we formulate economies of scale using nonlinear cost function of distances and demands, and analyze how the change of the transportation cost affects single hub location of air cargo using the generalized Weber problem on the regular demand points of one and two-dimensional region and the actual location of airport in East Asia. The optimal location will move to the largest demand points as the economies of scale in distances become larger or the economies of scale in demands become smaller. We confirm economies of scale in both distances and demands from the parameters estimated by the actual transportation cost in East Asia and find the optimal hub location of air cargo.
International conference proceedings, English

Pacific Ocean Route Optimization by Pittsburgh-style Learning Classifier System,
Iseya, S; Sato, K; Hattori, K; Takadama, K
ICCAS-SICE International Joint Conference 2009, 3A01-1, 2710-2715, 2009, Peer-reviwed
International conference proceedings, English

Sleep Stage Estimation by Learning Classifier System: Towards Nurse Care Support,
Hirose, K; Matsushima, H; Hattori, K; Takadama, K
ICCAS-SICE International Joint Conference 2009, 3A01-2, 2716-2721, 2009, Peer-reviwed
International conference proceedings, English

`Exploring Relationship Between Symbol Creation/ Understanding and Embodiment: Effect of Adjective in Communication Among Agents,
Inoue, T; Otani, M; Hattori, K; Takadama, K
ICCAS-SICE International Joint Conference 2009, 2A11-2, 1168-1173, 2009, Peer-reviwed
International conference proceedings, English

Proposal of Robust Wireless Overlay P2P Information Share System Based on Wireless Base Stations and Ad Hoc Devices
Hattori, K; Takadama, K
ICCAS-SICE International Joint Conference 2009, 3A01-6, 2738-2743, 2009, Peer-reviwed
International conference proceedings, English

Modeling Collective Adaptive Agent Design and its Analysis in Barnga Game,
Ushida, Y; Hattori, K; Takadama, K
14th International Conference on Economic Science with Heterogeneous Interacting Agents (ESHIA2009), 37-39, 2009, Peer-reviwed
International conference proceedings, English

Hybrid indoor location estimation system using image processing and WiFi strength
Kiyohiko Hattori; Ryousuke Kimura; Nobuo Nakajima; Tetuya Fujii; Youiti Kado; Bing Zhang; Takahiro Hazugawa; Keiki Takadama
PROCEEDINGS OF THE 2009 INTERNATIONAL CONFERENCE ON WIRELESS NETWORKS AND INFORMATION SYSTEMS, IEEE COMPUTER SOC, 406-+, 2009, Peer-reviwed, Recently, the service of cellular phone which is dependent on the location is spreading widely. Various techniques are suggested for the location estimation in indoor, however, indoor location estimation techniques have many subjects with a cost and an aspect of the accuracy. To deal with these subjects, we propose a quite new hybrid indoor location estimation method for paying attention to a smart phone with built-in wifi and camera. The proposal method is able to realized both highly accuracy of indoor location estimation and low cost by using the two-dimensional marker, wifi base stations, and the radio wave strength of adhoc communications. Verified by using simulation was able to demonstrate the effectiveness.
International conference proceedings, English

Awareness based filtering - Toward the Cooperative Learning in Human Agent Interaction -,
Yamaguchi, T; Nishimura, T; Takadama, K
ICCAS-SICE International Joint Conference 2009, 2A11-2, 1164-1167, 2009, Peer-reviwed
International conference proceedings, English

Network Evolution, Modifying Model for Public Transit Network
MAJIMA Takahiro; TAKADAMA Keiki; WATANABE Daisuke; KATUHARA Mitujiro
IPSJ SIG Notes, Information Processing Society of Japan (IPSJ), 2008, 85, 59-62, 11 Sep. 2008, The public transit network is comprised of lines and the organization of the lines affect to the performance of the public transit network. In this paper, a model generating effective public transit network is proposed. This model has two stages. At first stage, modified network evolution model in the research field of complex network produces the initial line set. At second stage, the line modifies its route as agent in the framework of the multi agent system. The proposed model provided better solutions than that of precedent work for a benchmark problem.
Japanese

Micro- and Macro-Level Validation in Agent-Based Simulation: Reproduction of Human-Like Behaviors and Thinking in a Sequential Bargaining Game
Keiki Takadama; Tetsuro Kawai; Yuhsuke Koyama
JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, J A S S S, 11, 2, Mar. 2008, Peer-reviwed, This paper addresses both micro- and macro-level validation in agent-based simulation (ABS) to explore validated agents that can reproduce not only human-like behaviors externally but also human-like thinking internally. For this purpose, we employ the sequential bargaining game, which can investigate a change in humans' behaviors and thinking longer than the ultimatum game (i.e., one-time bargaining game), and compare simulation results of Q-learning agents employing any type of the three types of action selections (i.e., the epsilon-greedy, roulette, and Boltzmann distribution selections) in the game. Intensive simulations have revealed the following implications: (1) Q-learning agents with any type of three action selections can reproduce human-like behaviors but not human-like thinking, which means that they are validated from the macro-level viewpoint but not from the micro- level viewpoint; and (2) Q-learning agents employing Boltzmann distribution selection with changing the random parameter can reproduce both human-like behaviors and thinking, which means that they are validated from both micro- and macro-level viewpoints.
Scientific journal, English

Analysis on Public Transport Networks of Railway, Subway and Waterbus in Japan from the Viewpoint of Complex Network
Majima, T; Takadama, K; Watanabe, D; Katsuhara, M
International Transactions on Systems Science and Applications (ITSSA), Vol. 3, No. 1, 19-26, 2008, Peer-reviwed
Scientific journal, English

マルチエージェントシステムと物流解析への応用
間島隆博; 高玉圭樹
海上技術安全研究所報告, Vol. 7, No. 4, 103-107, 2008, Peer-reviwed
Scientific journal, Japanese

Hierarchical Importance Sampling as Generalized Population Convergence
比護 貴之; 高玉 圭樹
情報処理学会学会誌, 49, SIG4. TOMO20, pp. 66--78, 2008, Peer-reviwed
Scientific journal, English

Analysis on Public Transport Networks of Railway, Subway and Waterbus in Japan from the Viewpoint of Complex Network
Majima, T; Takadama, K; Watanabe, D; Katsuhara, M
International Transactions on Systems Science and Applications (ITSSA), 3, 1, 19-26, 2008, Peer-reviwed
Scientific journal, English

Agent-Based Modeling of the Participant Nations and the Compliance Mechanism in the Emissions Trading
Nakata, T; Takadama, K; Watanabe, S
The Second World Congress on Social Simulation (WCSS'08), *, 1-12, 2008, Peer-reviwed
International conference proceedings, English

Simulation Analysis of the Participant Nations Behavior in the Emissions Trading
Tomohiro Nakada; Keiki Takadama; Shigeyoshi Watanabe
2008 PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-7, IEEE, 381-+, 2008, Peer-reviwed, This paper proposes a decision-making model of the participant nations using the reinforcement learning under uncertainty in the international emissions trading market, aim at clarifying the success conditions of the compliance mechanism in the emissions trading market. Intensive simulations have been revealed the following implications: (1) the trade of the emissions trading tended to stop that all nations set reduction commitment than 7% and (2) it is important to give high incentive for the improvement of the participant nations behavior.
International conference proceedings, English

Replicating Subject Experiments With Agents That Can Generalize Knowledge: Analyzing Bargaining Game Agents
Hatcho, Y; Takadama, K
The Second World Congress on Social Simulation (WCSS'08), *, 2008, Peer-reviwed
International conference proceedings, English

Toward robust deadlock avoidance method among multiple robots:Analyzing communication failure cases
Otani, M; Takadama, K
The 59th International Astronautical Congress (IAC2008), IAC08. B3., 2008, Peer-reviwed
International conference proceedings, English

Exemplar-Based Learning Classifier System: Towards Cargo Layout Optimization
Hiroyasu Matsushima; Keiki Takadama
2008 PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-7, IEEE, 936-941, 2008, Peer-reviwed, This paper focuses on generalization of Learning Classifier System (LCS) and explores the method for reducing the time of generalizing conscious rules that have the real number. For this purpose, we pay attention on exemplars (i.e., good examples) and, propose Exemplar-based LCS (ECS) that extracts useful exemplars as generalized rules by deleting unnecessary exemplars (some overlaping exemplars) as much as possible. To validate the effectiveness of ECS, this paper applies it to the cargo layout optimization problems. Intensive simulations have revealed the following implications; that (1) the gap between a center of gravity of HTV and its actual center is minimized by ECS in comparison with the other cases that employ 2000 exemplars and the randomly selected exemplars; (2) ECS can minimize the gap with the small numbers of exemplars (i.e., less than 2000 exemplars); and (3) such effectiveness of ECS is maintained even when the predetermined range of the match set is varied, which show the robustness of ECS against the parameter setting.
International conference proceedings, English

The deadlock avoidance method based on Leader-Follower relations among multiple robots in large-scale structure assembly
Masayuki Otani; Keiki Takadama
2008 PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-7, IEEE, 1432-1437, 2008, Peer-reviwed, This paper proposes the deadlock avoidance method where two robots that have different roles move together as on one set and change their roles between robots, and aims at investigating its effectiveness through simulation on constructing Space Solar Power Satellite (SSPS). Intensive simulations have revealed the following implications: (1) our proposed method enables robots to get out of deadlock situations and contributes to reducing the steps to assemble large-scale structure; and (2) the method is robust to the shape of the structure.
International conference proceedings, English

Generation of Public Transit Network by Network Evolution Model
Takahiro Majima; Keiki Takadama; Daisuke Watanabe; Mitujiro Katuhara
2008 PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-7, IEEE, 928-+, 2008, Peer-reviwed, Optimization of public transit network is an imperative subject to reduce the environmental burden. Furthermore, considering the fact that earthquakes occur frequently in Japan, it can be easily imagined that the commuter transportation system in and around Tokyo will be confused if the railway network is collapsed. The bus or waterbus is expected as substitute transportation mode for the railway under disaster circumstances. Thus, it is essential to organize routes for bus or waterbus immideately taking into account of devastated situations. A number of algorithms have been reported for optimal bus transit route network and most of them rely on heuristics approach. In this paper, a hybrid algorithm of heuristics and meta-heuristics approach organizing the bus transit route network is proposed. The heuristics algorithm is based on the network evolution model and the meta-heuristics algorithm relies on simulated annealing. The network evolution model is actively studied in the research field of complex network and it provides various findings on characteristic of real world networks. Since the network evolution model generates networks using local information, it has potential to be a fast algorithm to accommodate the operation under disaster circumstances. On the other hand, the SA algorithm is applied to avoid local minima posed from using only heuristics approach.
International conference proceedings, English

Hub airport location in air cargo system
Daisuke Watanabe; Takahiro Majima; Keiki Takadama; Mitujiro Katuhara
2008 PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-7, IEEE, 909-+, 2008, Peer-reviwed, Many of air cargo companies adapt the hub-and-spokes system where economies of scale exist in the transportation cost. In this paper, we analyze how the change of the transportation cost affects, single hub location of air cargo in the United States using the generalized Weber problem. We formulate economies of scale using nonlinear cost function of distances and demands. We confirm economies of scale in both distances and demands from the parameters estimated by the actual transportation cost, and the elasticity of distances has decreased greatly while the elasticity of demands hardly changes. The location of hub airport will move to the east coast which contains many large demand points as the economies of scale in distance become larger.
International conference proceedings, English

Single Hub location of air cargo using the generalized Weber model
Watanabe, D; Majima, T; Takadama, K; Katsuhara, M
The 11th International Symposium on locational decisions (ISOLDE XI), 137, 2008, Peer-reviwed
International conference proceedings, English

Hub airport location with nonlinear transportation cost function
Watanabe, D; Majima, T; Takadama, K; Katsuhara, M
The Third International Nonlinear Sciences Conference (INSC2008), *, 2008, Peer-reviwed
International conference proceedings, English

Towards Dynamic and Robust Robot Division of Tasks via Local Communication Among Robots - Application to Space Solar Power System Construction -
Okawa, T; Takadama, K
The Ninth International Symposium on Artificial Intelligence,Robotics and Automation in Space (i-SAIRAS08), **, 2008, Peer-reviwed
International conference proceedings, English

Dynamic Role change in Multiple Robots:Application to Space Solar Power Satellite Assembly
Takadama, K; Okawa, T; Hattori, K
The Second International Symposium on Robot and Artificial Intelligence, 134-144, 2008, Peer-reviwed
International conference proceedings, English

Exemplar-Based Learning Classifier System: Towards a Generalization of Expert Knowledge
Matsushima, H; Takadama, K
12th Asia-Pacific Workshop on Intelligent and Evolutionary Systems (IES'08), 62-70, 2008, Peer-reviwed
International conference proceedings, English

Modeling Knowledge Generalization Capability in Agent to Replicate Subject Experiment Result
Yasuyo Hatcho; Keiki Takadama
2008 PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-7, IEEE, 375-380, 2008, Peer-reviwed, Toward the agent model that can replicate human-like behaviors this paper aims at investigating a capability of our proposed agent model in terms of (1) knowledge size and (2) practical time for replicating them. For this purpose, we employ our agent that has the capability of (1) selecting which knowledge can be generalized among a lot of knowledge and (2) determining the timing when the selected knowledge should be generalized, and compare the result of our agents with these of agents employing heuristic methods with different knowledge generalization timing. Intensive simulations for comparisons in the sequential bargaining game have revealed the following implications: (1) both knowledge selection and knowledge generalization timing are critical for modeling agents; and (2) the proposed techniques enable agents to replicate the same subject experiment result, i.e., agents replicate it with a small number of knowledge (not sufficient numbers of knowledge) in practical times (the less iterations).
International conference proceedings, English

A partitioned random network agent model for organizational sectionalism studies
Kikuo Yuta; Yoshi Fujiwara; Wataru Souma; Keiki Takadama; Katsunori Shimohara; Osamu Katai
NEW FRONTIERS IN ARTIFICIAL INTELLIGENCE, SPRINGER-VERLAG BERLIN, 3609, 114-+, 2007, Peer-reviwed, This paper presents a new organization model that addresses the effects of networks on the sectionalism phenomenon, defined as excessive concern that members of a section have for the interests of their own section. No studies tackled the relationship between human communication networks and sectionalism. The points of our model design are: network distributed agents with a sense of values, extended random network structures, and a new index to monitor sectionalism. A homogeneous effect of communication networks and a heterogeneous effect of sectional specialization were also introduced into the model. Empirical results showed that sectionalism behavior and the performance of the proposed index were superior to conventional indices when capturing sectional structures. Finally, we showed one example of the availability of such a multi-agent network approach. Simulation results clearly illustrated the effect of cross-sectional links on sectionalism reduction by following a so-called "power law."
International conference proceedings, English

Waterbus route optimization by Pittsburgh-style Learning Classifier System
Keiji Sato; Keiki Takadama
PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-8, IEEE, 1146-+, 2007, Peer-reviwed, When a disaster occurs in the city center and roads and railroads etc. become unable to use, the waterbus has the great potential vehicles to transport passengers and several supplies. Since the number of passengers in such situation tend to change, according to the reconstruction degree of the city, effective and robust routes that can use two or more situations. To obtain such routes, this paper focuses on effective key routes to various situations, and proposes the method that put the pressure which decreases the number of routes. Through intensive simulations of five river stations, the following implications have been revealed, we get the routes which can transport passengers earlier than the case of not putting decreasing pressure of waterbus rout.
International conference proceedings, English

X-MAS: Validation tool based on meta-programming
Yutaka I. Leon Suematsu; Keiki Takadama; Katsunori Shimohara; Osamu Katai
AGENT-BASED APPROACHES IN ECONOMIC AND SOCIAL COMPLEX SYSTEMS IV, SPRINGER-VERLAG TOKYO, 3, 191-+, 2007, Peer-reviwed, Validation is an important issue in Agent-Based Modeling (ABM). Unfortunately, although indispensable, it is not a common practice in the community. The main reason is that there are no established validation procedures. In our previous work, we proposed the cross-element validation as a validation process that consists of detecting, analyzing and comparing the model's macro-behavior under different variations of its composite elements. This process requires performing several simulations of the model with modifications in some of its algorithms. Therefore, it is indispensable the availability of some tools that provides: (1) easy model implementation, (2) flexibility for easy model's elements exchange, and (3) construction of efficient code for accelerating the simulations. In order to support these requirements, this paper proposes the X-MAS toolkit, which facilitates the implementation and cross-element validation of ABM models.
International conference proceedings, English

Analyzing parameter sensitivity and classifier representations for real-valued XCS
Atsushi Wada; Keiki Takadama; Katsunori Shimohara; Osamu Katai
LEARNING CLASSIFIER SYSTEMS, SPRINGER-VERLAG BERLIN, 4399, 1-16, 2007, Peer-reviwed, To evaluate a real-valued XCS classifier system, we present a validation of Wilson's XCSR from two points of view. These are: (1) sensitivity of real-valued XCS specific parameters on performance and (2) the design of classifier representation with classifier operators such as mutation and covering. We also propose model with another classifier representation (LU-Model) to compare it with a model with the original XCSR classifier representation (CS-Model.) We did comprehensive experiments by applying a 6-dimensional real-valued multiplexor problem to both models. This revealed the following: (1) there are critical threshold on covering operation parameter (r(0)), which must be considered in setting parameters to avoid serious decreases in performance; and (2) the LU-Model has an advantage in smaller classifier population size within the same performance level over the CS-Model, which reveals the superiority of alternative classifier representation for real-valued XCS.
International conference proceedings, English

Tierra-based space system for robustness of bit inversion and program evolution
Ken Nonami; Keiki Takadama
PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-8, IEEE, 1151-1156, 2007, Peer-reviwed, This paper proposes the system that evolves to a better program by regarding a bit inversion caused by radiation as a chance of the mutation in program, and verifies the effectiveness of the proposed system by improving Tierra, digital life simulation for investing biological evolution. Concretely, the program improves to (1)achieve the goal received the outside from Tierra whose the goal is fixed as self-reproduction of the living thing, and (2)to introduce the mechanism that controls the reproduction and the lifespan of the program is added according to the goal achievement degree. As a result, a normal program was normal, and the program including a bug became normal.
International conference proceedings, English

Exploring quantitative evaluation criteria for service and potentials of new service in transportation: Analyzing transport networks of railway, subway, and waterbus
Keiki Takadama; Takahiro Majima; Daisuke Watanabe; Mitsujiro Katsuhara
INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2007, SPRINGER-VERLAG BERLIN, 4881, 1122-+, 2007, Peer-reviwed, This paper explores quantitative evaluation criteria for service and potentials of new service from the transportation viewpoint. For this purpose, we analyze transport networks of railway, subway, and waterbus, and have revealed the following implications: (1) efficiency criterion proposed by Latora [7,8] and centrality criterion in the complex network literature can be applied as quantitative evaluation criteria for service in a transportation domain; and (2) new services are highly embedded among networks, i.e., the analyses of the combined networks have the great potential for finding new services that cannot be found by analyzing a single network.
International conference proceedings, English

Counter example for Q-bucket-brigade under prediction problem
Atsushi Wada; Keiki Takadama; Katsunori Shimohara
LEARNING CLASSIFIER SYSTEMS, SPRINGER-VERLAG BERLIN, 4399, 128-143, 2007, Peer-reviwed, Aiming at clarifying the convergence or divergence conditions for Learning Classifier System (LCS), this paper explores: (1) an extreme condition where the reinforcement process of LCS diverges; and (2) methods to avoid such divergence. Based on our previous work that showed equivalence between LCS's reinforcement process and Reinforcement Learning (RL) with Function approximation (FA) method, we present a counter example for LCS with the Q-bucket-brigade based on the 11-state star problem, a counter example originally proposed to show the divergence of Q-learning with linear FA. Furthermore, the empirical results applying the counter example to LCS verified the results predicted from the theory: (1) LCS with the Q-bucket-brigade diverged under prediction problems, where the action selection policy was fixed; and (2) such divergence was avoided by using the implicit-bucket-brigade or applying residual gradient algorithm to the Q-bucket-brigade.
International conference proceedings, English

Can agents acquire human-like behaviors in a sequential bargaining game? Comparison of Roth's and Q-learning agents
Keiki Takadama; Tetsuro Kawai; Yuhsuke Koyama
MULTI-AGENT-BASED SIMULATION VII, SPRINGER-VERLAG BERLIN, 4442, 156-+, 2007, Peer-reviwed, This paper addresses agent modeling in multiagent-based simulation (MABS) to explore agents who can reproduce human-like behaviors in the sequential bargaining game, which is more difficult to be reproduced than in the ultimate game (i.e., one time bargaining game). For this purpose, we focus on the Roth's learning agents who can reproduce human-like behaviors in several simple examples including the ultimate game, and compare simulation results of Roth's learning agents and Q-learning agents in the sequential bargaining game. Intensive simulations have revealed the following implications: (1) Roth's basic and three parameter reinforcement learning agents with any type of three action selections (i.e., c-greed, roulette, and Boltzmann distribution selections) can neither learn consistent behaviors nor acquire sequential negotiation in sequential bargaining game; and (2) Q-learning agents with any type of three action selections, on the other hand, can learn consistent behaviors and acquire sequential negotiation in the same game. However, Q-learning agents cannot reproduce the decreasing trend found in subject experiments.
International conference proceedings, English

Analysis on transport networks of railway, subway and waterbus in japan
Takahiro Majima; Mitujiro Katuhara; Keiki Takadama
EMERGENT INTELLIGENCE OF NETWORKED AGENTS, SPRINGER-VERLAG BERLIN, 56, 99-+, 2007, Peer-reviwed, Characteristics of network in the real world have attracted a number of scientists and engineers. Various findings are given from recent studies on the real world network, sometimes called complex network. The properties on the complex network are revealed mainly by early studies focused on the un-weighted relational network, in which there is no weight on links or vertices, such as distance or traffic amount. Meanwhile, studies investigating weighted network has begun to appear in recent years. In these papers the weight denotes distance between vertices. The knowledge of complex network seems to provide us useful information to design and construct the transport networks. Our final purpose is to find an algorithm providing effective and optimized waterbus and bus network expected to reduce traffic congestions and increase redundancy of transport system under disaster circumstances. Toward this goal, this paper starts by investigating five transport networks, one railway, three subways and one hypothetical waterbus lines in Japan and their combinations from the viewpoint of complex network. Furthermore the role of waterbus network is made clear using measures in terms of complex network.
International conference proceedings, English

Route Generation of Public Transport System with Network Evolution
Majima, T; Takadama, K; Watanabe, D; Katsuhara, M
The 12th International Symposium on Artificial Life and Robotics (AROB'07), 585-588, 2007, Peer-reviwed
International conference proceedings, English

Towards Fault Tolerant of Space Solar Power Satellite: Challenge Issues and Applicability of Distributed Fault Diagnosis
Takadama, K; Hattori; K. Murata; S; Furuya, H
The First International Conference on Robot and Artificial Intelligence, 22-31, 2007, Peer-reviwed
International conference proceedings, English

Micro- and Macro-level Validation in Agent-based simulation:Reproduction of Human-like Behaviors and Thinking in a Sequential Bargaining Game
Takadama, K; Kawai, T; Koyama, Y
The Third International Model-to-Model Workshop (M2M'07), 46-65, 2007, Peer-reviwed
International conference proceedings, English

Characteristic and Application of Network Evolution Model for Public Transport Network
Majima, T; Takadama, K; Watanabe, D; Katsuhara, M
11th Asia-Pacific Workshop on Intelligent and Evolutionary Systems (IES2007), *, 2007, Peer-reviwed
International conference proceedings, English

Network Evolution Model for Route Design of Public Transport System and its Applications
Majima, T; Takadama, K; Watanabe, D; Katsuhara, M
The Second Workshop on Emergent Intelligence on Networked Agents(WEIN'07), 57-69, 2007, Peer-reviwed
International conference proceedings, English

Resampling-based Population Mechansism for Evolutionary Algorithms based on Probability Models
Higo, T; Takadama, K
11th Asia-Pacific Workshop on Intelligent and Evolutionary Systems (IES2007), *, 2007, Peer-reviwed
International conference proceedings, English

Hierarchical importance sampling instead of annealing
Takayuki Higo; Keiki Takadama
2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, IEEE, 134-+, 2007, Peer-reviwed, This paper proposes a novel method, Hierarchical Importance Sampling (HIS), which can be used instead of converging the population for Evolutionary Algorithms based on Probabilistic Models (EAPM). In HIS, multiple populations are simulated simultaneously so that they have different diversities. This mechanism allows HIS to obtain promising solutions with various diversities. Experimental comparisons between HIS and the annealing (i.e., general EAPM) have revealed that HIS outperforms the annealing when applying to a problem of a 2D Ising model, which have many local optima. Advantages of HIS can be summarized as follows: (1) Since populations do not need to converge and do not change rapidly, HIS can build probability models with stability; (2) Since samples with better cost function values can be used for building probability models in HIS, HIS can obtain better probability models; (3)HIS can reuse historical results, which are normally discarded in the annealing.
International conference proceedings, English

Waterbus route optimization by Pittsburgh-style learning classifier system
Keiji Sato; Keiki Takadama
Proceedings of the SICE Annual Conference, 1150-1154, 2007, Peer-reviwed, When a disaster occurs in the city center and roads and railroads etc. become unable to use, the waterbus has the great potential vehicles to transport passengers and several supplies. Since the number of passengers in such situation tend to change, according to the reconstruction degree of the city, effective and robust routes that can use two or more situations. To obtain such routes, this paper focuses on effective key routes to various situations, and proposes the method that put the pressure which decreases the number of routes. Through intensive simulations of five river stations, the following implications have been revealed, we get the routes which can transport passengers earlier than the case of not putting decreasing pressure of waterbus rout. © 2007 SICE.
International conference proceedings, English

Tierra-based space system for robustness of bit inversion and program evolution
Ken Nonami; Keiki Takadama
Proceedings of the SICE Annual Conference, 1155-1160, 2007, Peer-reviwed, This paper proposes the system that evolves to a better program by regarding a bit inversion caused by radiation as a chance of the mutation in program, and verifies the effectiveness of the proposed system by improving Tierra, digital life simulation for investing biological evolution. Concretely, the program improves to (1)achieve the goal received the outside from Tierra whose the goal is fixed as self-reproduction of the living thing, and (2)to introduce the mechanism that controls the reproduction and the lifespan of the program is added according to the goal achievement degree. As a result, a normal program was normal, and the program including a bug became normal. © 2007 SICE.
International conference proceedings, English

A robustness distributed system with sensing and fault detection for large-scale sensor networks
Kiyohiko Hattori; Keiki Takadama; Satoshi Murata; Hiroshi Furuya
PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-8, IEEE, 1157-1161, 2007, Peer-reviwed, In this paper, we propose a robustness data collection and fault detection method for large-scale sensor networks, which consist of ten thousand sensor nodes. The sensor network has some special features as multihop communication, data aggregation, and high error data rate caused by the huge number of nodes. To tackle this issue, we propose a distributed and self-organized method for data collecting and diagnosis. Our method has two features that are token nodes and limited broadcast for the data sharing and collecting. The token node is a special node that can make a limited broadcast packet for data collecting and sharing. Limited broadcast is a limited multihop number and we apply only two hops on this research. Those features can prevent a flood of broadcast packet. To clear the capability of our method, we make a few simulations and compare with other data collecting and diagnose method. As the result, our proposed method has better performance than other method.
International conference proceedings, English

自律分散型故障診断手法の提案 － on-line分散型診断手法との比較 －
服部聖彦; 高玉圭樹; 村田 智; 古谷 寛; 上野浩史; 稲場典康; 小田光茂
人工知能学会学会誌, 21, 4, 417-426, 2006, Peer-reviwed
Scientific journal, Japanese

行動価値に着目した学習分類子システムの改善: マルチエージェント強化学習への接近
井上寛康; 高玉 圭樹; 下原 勝憲
情報処理学会誌, Information Processing Society of Japan (IPSJ), 47, 5, 1483-1492, 2006, Peer-reviwed, XCS is the newest Learning Classifier System (LCS), and at present it can only be used for deterministic transition environments. This paper proposes XCS-QT as a modified LCS that can appropriately generalize its experience and can be used for multi-agent environments that are more complex than deterministic transition environments. We then show the system's advantage via simulation experiments using quasi-tree problems and hunter problems. Through the experiments, we demonstrate that there are several reasons why XCS cannot work very well in multi-agent environments, and that XCS-QT can overcome those problems.
Scientific journal, Japanese

マルチエージェントに基づくカーゴレイアウトシステム：日本がイニシアティブをとる次世代運用システムに向けて
高玉 圭樹
人工知能学会学会誌, 人工知能学会, 21, 1, 39-44, 2006, Peer-reviwed
Scientific journal, Japanese

Detecting Failure of Spacecraft Using Separated States in Particle Filters
Takadama, K; Murakami, T; Kawahara, Y
The 25th International Symposium on Space Technology and Science:(ISTS'06), CD-ROM, 2006, Peer-reviwed
International conference proceedings, English

Neighbor based Parents Selection for Real-coded Genetic Algorithms
Higo, T; Takadama, K
Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium on Advanced Intelligent Systems, 1167-1172, 2006, Peer-reviwed
International conference proceedings, English

Dual-structured Classifier System Mediating XCS and Gradient Descent based Update
Wada, A; Takadama, K; Shimohara, K
The 9th International Workshop on Learning Classifier Systems(IWLCS'06), CD-ROM, 2006, Peer-reviwed
International conference proceedings, English

Community of Practice under Learning Classifier Systems
Suematsu, Y.I.L; Takadama, K; Shimohara, K; Katai, O
The 9th International Workshop on Learning Classifier Systems (IWLCS'06), CD-ROM, 2006, Peer-reviwed
International conference proceedings, English

Analyzing Robustness in Multiagent Reinforcement Learning - A comparison between Profit Sharing and Q-Learning -,
Nehashi, T; Takadama, K; Miyazaki, K
The 11th International Symposium on Artificial Life and Robotics (AROB'06), 47-50, 2006, Peer-reviwed
International conference proceedings, English

Concept of Inflatable Tensegrity for Large Space Structures
Furuya, H; Nakahara, M; Murata, S; Jodoi D; Terada, Y; Takadama, K
The 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Material Conference, 1-3, 2006, Peer-reviwed
International conference proceedings, English

Deployment Characteristics of Multi-Cellular Inflatable Tubes
Furuya, H; Nakahara, M; Murata, S; Takadama, K
The 57th International Astronautical Congress (IAC'06), 1-5, 2006, Peer-reviwed
International conference proceedings, English

Comparison between Self-organization with Sampling and Genetic Algorithms in multi-modal function
Higo, T; Takadama, K; Katuhara, M; Majima, T
The 11th International Symposium on Artificial Life and Robotics (AROB'06), 374-377, 2006, Peer-reviwed
International conference proceedings, English

Toward guidelines for modeling learning agents in multiagent-based simulation: Implications from Q-learning and sarsa agents
K Takadama; H Fujita
MULTI-AGENT AND MULTI-AGENT-BASED SIMULATION, SPRINGER-VERLAG BERLIN, 3415, 159-172, 2005, Peer-reviwed, This paper focuses on how simulation results are sensitive to agent modeling in multiagent-based simulation (MABS) and investigates such sensitivity by comparing results where agents have different learning mechanisms, i.e., Q-learning and Sarsa, in the context of reinforcement learning. Through an analysis of simulation results in a bargaining game as one of the canonical examples in game theory, the following implications have been revealed: (1) even a slight difference has an essential influence on simulation results; (2) testing in static and dynamic environments highlights the different tendency of results; and (3) three stages in both Q-learning and Sarsa agents (i.e., (a) competition, (b) cooperation; and (c) learning impossible) are found in the dynamic environment, while no stage is found in the static environment. From these three implications, the following very rough guidelines for modeling agents can be derived: (1) cross-element validation for specifying key factors that affect simulation results; (2) a comparison of results between the static and dynamic environments for determining candidates to be investigated in detail; and (3) sensitive analysis for specifying applicable range for learning agents.
Scientific journal, English

Evaluation criteria for learning mechanisms applied to agents in a cross-cultural simulation
YIL Suematsu; K Takadama; K Shimohara; O Katai; K Arai
Agent-Based Simulation: From Modeling Methodologies to Real-World Applications, SPRINGER-VERLAG TOKYO, 1, 89-98, 2005, Peer-reviwed, In problems with non-specific equilibrium, common in social sciences, the processes involved in learning mechanisms can produce quite different outcomes. However, it is quite difficult to define which of the learning mechanisms is the best. When considering the case of a cross-cultural environment, it is necessary to evaluate how adaptation to different cultures occurs while keeping, at some level, the cultural diversity among the groups. This paper focuses on identifying an evaluation criterion using a comparison of various learning mechanisms that can manage the trade-off between adaptation to a new culture and the preservation of cultural diversity. Results show that: (a) For small and gradual accuracy from a less accurate learning mechanism, there is a tiny reduction in the diversity while the convergence time drops rapidly. For an accuracy level close to the most accurate learning mechanism, a reduction of the convergence time can be minor, while the diversity drops rapidly; (b) The evaluation of learning mechanism that performs better for fast converging while simultaneously keeping a good diversity before the convergence was performed graphically.
International conference proceedings, English

社会組織シミュレーションにおける妥当性検証： エージェントのモデリングから始めよう
高玉 圭樹
組織科学, 白桃書房, 39, 1, 15-25, 2005, Peer-reviwed
Scientific journal, Japanese

実数値学習分類子システムの分析：XCSにおける実数値分類子表現および表現に固有なパラメータの検証
和田充史; 高玉圭樹; 下原勝憲; 片井修
人工知能学会学会誌, 20, 1, 57-66, 2005, Peer-reviwed
Scientific journal, Japanese

Autonomous symbol acquisition through agent communication
A Wada; K Takadama; K Shimohara; O Katai
Recent Advances in Simulated Evolution and Learning, WORLD SCIENTIFIC PUBL CO PTE LTD, 2, 711-728, 2004, Peer-reviwed, In this chapter, we propose a multi-agent system aiming at autonomous symbol acquisition, in which agents acquire symbols through communication instead of symbols being given a priori by the designer. Based on this idea, we extended Steels's language acquisition model to develop a new model featuring three mechanisms: (a) symbol matching; (b) symbol creation; and (c) concept selection. Intensive simulation revealed the following implications: (1) the degree of trade-off between communication success and required lexicon size can be decreased by matching all possible combinations in symbol matching; (2) symbol creation of hearer agents plays a significant role in symbol acquisition, while speaker agents do not; (3) the speed of symbol creation depends on the method used for this step, but it is not related to the trade-off between communication success and lexicon size; and (4) concept selection can also be applied to resolve the trade-off between communication success and required lexicon size.
International conference proceedings, English

Interpretation by Implementation for Understanding a Multiagent Organization
K. Takadama; T. Terano; K. Shimohara
Computational and Mathematical Organization Theory (CMOT) , Kluwer Academic Publishers, 9, 1, 19-35, 2004, Peer-reviwed
Scientific journal, English

Specifying a Failure Area in a Large-Scale Space Structure: Which Method is Better - Majority or Individual ?
Kiyohiko Hattori; Keiki Takadama; Hiroshi Ueno; Mitsushige Oda
IEEJ Transactions on Electronics, Information and Systems, 124, 10, 2019-2026, 2004, Peer-reviwed, This paper analyzes the capabilities of two methods that specify a failure area in a large-scale space structure: (1) the adjoining module decision (AMD) method proposed by our previous research
and (2) a Byzantine-based decision (BBD) method which is a general method in the context of a distributed processing approach. In this paper, we investigate methods that specify a failure area from the viewpoint of a distributed processing approach. Through intensive simulations, we finally concluded that the adjoining module decision method has better capability than a Byzantine-based decision method. Other implications are summarized as follows
(1) A Byzantine-based decision method cannot specify a failure area if the number of broken modules is over 1/3 of the connected modules, while the adjoining module decision method can
(2) A Byzantine-based decision method requires more time to decide a failure area than the adjoining module decision method
(3) Neither methods can specify a failure area stably when methods do not disconnect broken modules
and (4) an AMD method does not depend on the shape of the failure area, which indicates that the AMD method is robust in many cases. © 2004, The Institute of Electrical Engineers of Japan. All rights reserved.
Scientific journal, English

Comparing Learning Classifier System and Reinforcement Learning with Function Approximation
Atsushi Wada; Katsunori Shimohara; Keiki Takadama; Osamu Katai
IEEJ Transactions on Electronics, Information and Systems, 124, 10, 2034-2039, 2004, Peer-reviwed, As a first step toward an analysis of the capabilities of adaptive systems, including learning and evolution, we focus on the Learning Classifier System (LCS) and compare it with Reinforcement Learning (RL) that adopts the Function Approximation (FA) method. An analysis of this comparison found an equivalence of learning processes between both the two models, which brings the mathematical framework of the LCS's learning process to the level of RL with FA. Our analysis also clarified the limitations of the results. © 2004, The Institute of Electrical Engineers of Japan. All rights reserved.
Scientific journal, English

Cross-element validation in multiagent-based simulation: Switching learning mechanisms in agents
K Takadama; YL Suematsu; N Sugimoto; NE Nawa; K Shimohara
JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, J A S S S, 6, 4, Oct. 2003, Peer-reviwed, The validity of simulation results remains an open problem in multiagent-based simulation (MABS). Since such validity is based on the validation of computational models, we propose a cross-element validation method that validates computational models by investigating whether several models can produce the same results after changing an element in the agent architecture. Specifically, this paper focuses on learning mechanisms applied to agents as one of the important elements and compares three different MABSs employing either an evolutionary strategy (ES), a learning classifier system (LCS), or a reinforcement learning (RL). This type of validation is not based on the between-models addressed in conventional research but on a within-model. A comparison of the simulation results in a bargaining game, one of the fundamental examples in game theory, reveals that (1) computational models are minimally validated in the case of ES- and RL-based agents; and (2) learning mechanisms that enable agents to acquire their rational behaviors differ according to the knowledge representation (i.e., the strategies in the bargaining game) of the agents. Concretely, we found that (2-a) the ES-based agents derive the same tendency in game theory but the LCS-based agents cannot in the case of employing continuous knowledge representation; and (2-b) the same ES-based agents cannot derive the same tendency in game theory but the RL-based agents derive it in the case of employing discrete knowledge representation.
Scientific journal, English

Analyzing the agent-based model and its implications
YIL Suematsu; K Takadama; NE Nawa; K Shimohara; O Katai
ADVANCES IN COMPLEX SYSTEMS, WORLD SCIENTIFIC PUBL CO PTE LTD, 6, 3, 331-347, Sep. 2003, Peer-reviwed, Agent-based models (ABMs) have been attracting the attention of researchers in the social sciences, becoming a prominent paradigm in the study of complex social systems. Although a great number of models have been proposed for studying a variety of social phenomena, no general agent design methodology is available. Moreover, it is difficult to validate the accuracy of these models. For this reason, we believe that some guidelines for ABMs design must be devised; therefore, this paper is a first attempt to analyze the levels of ABMs, identify and classify several aspects that should be considered when designing ABMs. Through our analysis, the following implications have been found: (1) there are two levels in designing ABMs: the individual level, related to the design of the agents' internal structure, and the collective level, which concerns the design of the agent society or macro-dynamics of the model; and (2) the mechanisms of these levels strongly affect the outcomes of the models.
Scientific journal, English

Acquisition of a specialty in multi-agent learning - Approach from learning classifier system
H Inoue; K Takadama; K Shimohara; O Katai
2003 IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN ROBOTICS AND AUTOMATION, VOLS I-III, PROCEEDINGS, IEEE, 1090-1095, 2003, Peer-reviwed, We focus on a multi-agent learning where plural agents acquire different specialties to achieve the system goal. This allows the system to solve the deadlock or malfunction problems where agents cannot realize the system goal due to a lack of coodination among the sub-goals pursued by the agents. To this end, this paper proposes an algorithm based on the Learning Classifier System that divides the sub-tasks that agents specialize in. Through experiments, it is shown that agents with the algorithm have greater potential compared to agents using the conventional Learning Classifier System when there axe only a few agents in the system or the environment is too large for the conventional Learning Classifier System to learn effectively.
International conference proceedings, English

Analyzing state space segmentation in Learning Classifier System
A Wada; K Takadama; K Shimohara; O Katai
SICE 2003 ANNUAL CONFERENCE, VOLS 1-3, IEEE, 1487-1491, 2003, Peer-reviwed, We present an analysis on state space segmentation for the Learning Classifier System (LCS). An LCS model is proposed that can segment input state space into variable granularity. A preliminary experiment on a real-valued 6-multiplexor problem is conducted which result revealed that small granularity of segmentation affects the size of the classifier population by causing it to increase.
International conference proceedings, English

The X-MAS SYSTEM: Toward simulation systems for cross-model-validation in multiagent-based simulations
YIL Suematsu; K Takadama; NE Nawa; K Shimohara; O Katai
MEETING THE CHALLENGE OF SOCIAL PROBLEMS VIA AGENT-BASED SIMULATION, SPRINGER-VERLAG TOKYO, 171-184, 2003, Peer-reviwed, The huge number of simulation models available in different scientific communities shows the prominent role that simulations are playing in the study of complex social systems. However, model validation is not an established practice among communities, but it is indispensable for assuring reliable models and results when studying a certain social phenomenon. In order to help researchers in validation processes, this paper proposes the Cross-model validation for MultiAgent-based Simulation (X-MAS) System, a toolkit developed for supporting validation of models and facilitating the implementation of complex social system simulations by addressing the following three aspects: (1) rich object-oriented library for cross-model validation, simultaneously providing X-MAS with verification and validation capabilities. For validation purpose, X-MAS supplies with an agent structure embedding several kind of elements, such as different learning mechanisms and knowledge representation schemes, (2) high-level programming skills are not required for rapid prototyping, and (3) framework facilities for the promotion of an effective cumulative scientific process, making it possible to evaluate and verify different models, permitting their exchange from different scientific communities, and stimulating the replication of results and their further verification and validation. The effectiveness of X-MAS is shown by investigating a bargaining model, a well-study model in game theory.
International conference proceedings, English

Towards verification and validation in multiagent-based systems and simulations: Analyzing different learning bargaining agents
K Takadama; YL Suematsu; N Sugimoto; NE Nawa; K Shimohara
MULTI-AGENT-BASED SIMULATION III, SPRINGER-VERLAG BERLIN, 2927, 26-42, 2003, Peer-reviwed, Verification and validation (V&V) is a critical issue in both multi-agent systems (MAS) and agent-based social simulation (ABSS). As the first step towards V&V methods for MAS and ABSS, this paper investigates whether different computational models can produce the same results. Specifically, we compare three computational models with different learning mechanisms in a multiagent-based simulation and analyze the results of these models in a bargaining game as one of the fundamental examples in game theory. This type of V&V is not based on the between-models addressed in conventional research, but on a within-model. A comparison of the simulation results reveals that (1) computational models and simulation results are minimally verified and validated in the case of ES(evolutionary strategy)- and RL(reinforcement learning)-based agents; and (2) learning mechanisms that enable agents to acquire their rational behaviors differ according to the knowledge representation (i.e., the strategies in the bargaining game) of the agents.
Scientific journal, English

A reinforcement learning approach to fail-safe design for multiple space robots - cooperation mechanism without communication and negotiation schemes
K Takadama; S Matsumoto; S Nakasuka; K Shimohara
ADVANCED ROBOTICS, VSP BV, 17, 1, 21-39, 2003, Peer-reviwed, This paper explores a fail-safe design for multiple space robots, which enables robots to complete given tasks even when they can no longer be controlled due to a communication accident or negotiation problem. As the first step towards this goal, we propose new reinforcement learning methods that help robots avoid deadlock situations in addition to improving the degree of task completion without communications via ground stations or negotiations with other robots. Through intensive simulations on a truss construction task, we found that our reinforcement learning methods have great potential to contribute towards fail-safe design for multiple space robots in the above case. Furthermore, the simulations revealed the following detailed implications: (i) the first several planned behaviors must not be reinforced with negative rewards even in deadlock situations in order to derive cooperation among multiple robots, (ii) a certain amount of positive rewards added into negative rewards in deadlock situations contributes to reducing the computational cost of finding behavior plans for task completion, and (iii) an appropriate balance between positive and negative rewards in deadlock situations is indispensable for finding good behavior plans at a small computational cost.
Scientific journal, English

An Interpretation of Brand Marketing in Agent-Based Simulation(Econophysics)
Takadama Keiki; Tsujinaka Naohiro; Shimohara Katsunori
Journal of the Japan Society for Simulation Technology, Japan Society for Simmulation Technology, 21, 2, 113-122, 15 Jun. 2002, This paper proposes Agent-based Brand Marketing (ABM) Model and discusses an important issue of validation on an interpretation of simulation results. In this model, consumer agents purchase products which are developed by company agents. Through several interpretations of markets, the following implications have been revealed: (1) when interpreting simulation results from the viewpoint of time, it is important to compare two markets which structure differs from each other rather than two markets where the number of iterations differs from each other; (2) when interpreting simulation results from the viewpoint of learning of agents, it is important to analyze varieties of behaviors of agents rather than their learning speed.
Japanese

URL

Problems and Cumulative Progress in Agent-Based Simulation : What Should We Consider in Future Agent-Based Simulation?
TAKADAMA Keiki; SHIMOHARA Katsunori
Social and Economic Systems Studies: The Journal of the Japan Association for Social and Economic Systems Studies, The Japan Association for Social and Economic Systems Studies, 21, 68-84, 2002, Agent-based simulation is one of the potential approaches for understanding complex organizational and social phenomena. However, this approach has a serious problem characterized as a lack of cumulative progress. To tackle this problem, this paper investigates the main causes of the problem and explores some solutions. Through an analysis, the following implications are revealed: (1) cumulative progress in an agent-based approach is promoted by (a) the sharing of common results, (b) the development of standard computational models, (c) the replicating of older works, and/or (d) the creation of standard evaluation criteria; (2) our approach has great potential for promoting cumulative progress in an agent-based approach in terms of achieving the above four points; and (3) the factors found in our approach have the high possibility of being a fundamental process in the real world, supporting the KISS principle, and being utilized as toolkits.
Japanese

Robustness in Organizational-Learning Oriented Classifier System
TAKADAMA K.
Journal of Soft Computing, 6, 3, 229-239, 2002

The hare and the tortoise - Cumulative progress in agent-based simulation
K Takadama; K Shimohara
AGENT-BASED APPROACHES IN ECONOMIC AND SOCIAL COMPLEX SYSTEMS, I O S PRESS, 72, 3-14, 2002, Peer-reviwed, Agent-based simulation is one of the potential approaches for understanding complex organizational and social phenomena. However, this approach has a serious problem characterized as a lack of cumulative progress. To tackle this problem, this paper investigates the main causes of the problem and explores some solutions. Through an analysis, the following implications are revealed: (1) cumulative progress in an agent-based approach is promoted by (a) the sharing of common results, (b) the development of standard computational models, (c) the replicating of older works, and/or (d) the creation of standard evaluation criteria; our approach has great potential for promoting cumulative progress in an agent-based approach in terms of achieving the above four points; and (2) the factors found in our approach have the high possibility of being a fundamental process in the real world, supporting the KISS principle, and being utilized as toolkits.
International conference proceedings, English

Nongovernance rather than governance in a multiagent economic society
K Takadama; T Terano; K Shimohara
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 5, 5, 535-545, Oct. 2001, Peer-reviwed, This paper explores how to achieve goals at the macro level without controlling self-interested economic agents at the micro level and investigates the effectiveness of our claim suggesting that we make use of properties arising from interaction among economic agents to address the above issue. Intensive experiments on a complex domain problem have found the following implications: 1) as an institution design, it is important not to control economic agents at the micro level, but to promote them to self-activate in order to achieve goals at the macro level and 2) as a role of an administrative party like a government, it is important to have a clear view to determine which results are good because the timing for finding such results depends on the environmental situation and there is no guarantee that these results will converge. Other implications are summarized as follows: 1) it is important to remove evaluation level intervention to find good results, while it is important to introduce this intervention to reduce costs and 2) behavior intervention does not contribute to finding good results nor reducing costs.
Scientific journal, English

Towards a Multiagent Design Principle-Analyzing an Organizational-Learning Oriented Classifier System-
TAKADAMA K.
Soft Computing Agents : New Trends for Designing Autonomous Systems, Springer-Verlag, 2001

Exploration and exploitation trade-off in multiagent learning
K Takadama; K Shimohara
ICCIMA 2001: FOURTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND MULTIMEDIA APPLICATIONS, PROCEEDINGS, IEEE COMPUTER SOC, 133-137, 2001, Peer-reviwed, This paper focuses on the trade-off between exploration and exploitation in multiagent learning and explores some fundamental factors that contribute to clarifying this trade-off. Through inventive simulations on distributed constraint satisfaction problems in multiagent environments, the following implications are revealed: (1) the trade-off between exploration and exploitation at the collective level is not easy to be solved when considering the trade-off at the individual level; but (2) the trade-off at the collective level can be solved by introducing a simple gradient search in the trade-off at the individual level.
International conference proceedings, English

Agent Architecture based on an interactive self-reflection classifier System
Keiki TAKADAMA; H. Inoue; Katsunori SHIMOHARA; Michio OKADA; Osamu KATAI
International Journal of Artificial Life and Robotics, 5, 2, 103-108, 2001, Peer-reviwed
Scientific journal

Computational Analysis of Brand Marketing
TAKADAMA,K; TSUJINAKA, N; K.SHIMOHARA
Lead, Computational Analysis of Social and Organizational System 2001 (CASOS2001), 38-40, 2001, Peer-reviwed

Which organizational knowledge is useful: rare, medium, or well-done? Comparison of different levels of organizational knowledge in multiagent environments
K Takadama; T Terano; K Shimohara
IECON 2000: 26TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-4, IEEE, 2891-2896, 2000, Peer-reviwed, This paper focuses on several levels of organizational knowledge and investigates the characteristics of each level in multiagent environments. A careful investigation of the characteristics has revealed the following implications: (1) a moderately structured level of organizational knowledge improves solutions in an, individual evaluation and reduces computational costs in an organizational evaluation as a comparison when reusing roughly or well structured levels of organizational knowledge: and (2) the moderately structured level of organizational knowledge has great potential for further improvement of solutions or computational costs even in strict cases where good solutions or small computational costs have been, already found.
International conference proceedings, English

How to autonomously decide boundary between self and others?
K Takadama; H Inoue; K Shimohara
IECON 2000: 26TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-4, IEEE, 2903-2908, 2000, Peer-reviwed, This paper extends the Learning Classifier, System, (LCS) to introduce the mechanism for recognizing a current situation by determining a boundary between self and others. and investigates its effectiveness in an interaction with an agent. Intensive simulations for, adapting or? interacting agent by acquiring its internal model hum revealed the following implications: (1) the proposed mechanism gives a higher adaptation to the integrating agent than a random mechanism, the conventional LCS. and previously proposed mechanisms: (2) the proposed mechanism, keeps its effectiveness even in a complex internal model of an agent: and (3) the proposed mechanism has the potential to provide autunomy in terms of the precise recognition of the current situation.
International conference proceedings, English

On the evolution of interaction rules in a canonical auction market with simple bidding agents
NE Nawa; K Takadama; K Shimohara; O Katai
IECON 2000: 26TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-4, IEEE, 2897-2902, 2000, Peer-reviwed, Auction markets have been continually attracting attention in the field of economics due to their interesting properties as trading institutions. The recent boom of electronic markets over the Internet has also sparked related research in the field of artificial intelligence (AI). The main aspects investigated concerning electronic markets are the construction of automated negotiating agents, and the design of mechanisms and protocol rules to coordinate their interaction. In this paper, the construction of rules, by a genetic algorithm, to coordinate the bidders interaction in a canonical auction market is investigated, Auction rules have been deeply investigated in scenarios with human actors, where commonsensical protocols naturally prevail, restricting the possibilities of using idiosyncratic interaction procedures. By means of computational experiments, we show that in a hypothetical situation where the bidders follow very simple strategies, non-conventional auction rules can perform better than conventional protocols.
International conference proceedings, English

Toward Emergent Problem Solving by Distributed Classifier Systems Based on Organizational Learning
TAKADAMA Keiki; TERANO Takao; SHIMOHARA Katsunori; HORI Koichi; NAKASUKA Shinichi
計測自動制御学会論文集, 計測自動制御学会, 35, 11, 1486-1495, 30 Nov. 1999
Japanese

Grammatical Learning Model for Adaptive Collective Behaviors in Multiple Robots.
Keiki Takadama; Koichiro Hajiri; Tatsuya Nomura; Katsunori Shimohara; Shinichi Nakasuka
Grammatical Models of Multi-Agent Systems, Gordon and Breach Science Publishers, 343-355, 1999, Peer-reviwed

Making Organizational Learning Operational : Implication from Learning Classifier System
Takao Terano
Journal of Computational and Mathematical Organization Theory, 5, 3, 229-252, 1999

How to design good learning agents in organization
K Takadama; T Terano; K Shimohara
GECCO-99: PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, MORGAN KAUFMANN PUB INC, 2, 1398-1405, 1999, Peer-reviwed, This paper categorizes four types of multiagent learning in terms of both goals and evaluations in agents, and investigates the characteristics of each categorization to find an effective type for designing learning agents. Since the characteristics in this categorization are affected by the learning mechanisms of agents, the characteristics are investigated by referring to organizational learning in organization and management science as one of methods. Through intensive simulations on a complex domain problem, the following implication has been revealed: agents that pursue their own goals and are evaluated according to their total results show high performance in comparison with other types of agents when all learning mechanisms in the organizational learning are employed.
International conference proceedings, English

Organizational learning agents for task scheduling in space crew and robot operations
K Takadama; H Kasahara; LC Huang; M Watabe; H Ii; K Shimohara; S Nakasuka
ISAIRAS '99: FIFTH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE, ROBOTICS AND AUTOMATION IN SPACE, EUROPEAN SPACE AGENCY, 440, 561-568, 1999, Peer-reviwed, This paper explores rescheduling and reorganization abilities of our organizational learning model in the following two important applications in space: crew task scheduling in a space shuttle/station and task planning for truss construction with multiple space robots. Through intensive simulations of the above two tasks, the following experimental results have been obtained: (1) Our model provides good feasible schedules quickly in the case of rescheduling, and it keeps the computational cost for rescheduling low; (2) Plans generated by our model keep or recover efficiency in tasks when robots are added, removed, or exchanged among robot groups; and (3) The integration of(a) learning mechanisms, (b) rule based systems with evolutionary approaches, and (c) multiagent approaches is effective in rescheduling/replanning problems.
International conference proceedings, English

Printed circuit board design via organizational-learning agents
K Takadama; S Nakasuka; T Terano
APPLIED INTELLIGENCE, KLUWER ACADEMIC PUBL, 9, 1, 25-37, Jul. 1998, This gaper proposes a novel evolutionary computation model: Organizational-Learning Oriented Classifier System (OCS), and describes its application to Printed Circuit Boards (PCBs) redesign problems in a computer aided design (CAD). Using the conventional CAD systems which explicitly decide the parts' placements by a knowledge base, the systems cannot effectively place the parts as done by human experts. Furthermore, the supports of human experts are intrinsically required to satisfy the constraints and to optimize a global objective function. However, in the proposed model OCS, the parts generate and acquire adaptive behaviors for an appropriate placement without explicit control. In OCS, we focus upon emergent processes in which the parts dynamically form an organized group with autonomously generating adaptive behaviors through local interaction among them. Using the model OCS, we have conducted intensive experiments on a practical PCB redesign problem for electric appliances. The experimental results have shown that: (1) it has found the feasible solutions of the same level as the ones by human experts, (2) solutions are locally optimal, and also globally better than the ones by human experts with regard to the total wiring length, and (3) the solutions are more preferable than those in the conventional CAD systems.
Scientific journal, English

Multiagent reinforcement learning with organizational-learning oriented Classifier System
K Takadama; S Nakasuka; T Terano
1998 IEEE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION - PROCEEDINGS, IEEE, 63-68, 1998, Peer-reviwed, Organizational-learning oriented Classifier System (OCS) is a new architecture proposed by us for an evolutionary computational model. We have shown its effectiveness in large scale problems with Printed Circuit Boards (PCBs) re-design in the Computer Aided Design (CAD). This paper proposes a novel reinforcement learning method for multiagents with OCS for more practical and engineering use. To validate the effectiveness of our method, we have conducted experiments on real scale PCBs design problems for electric appliances. The experimental results have suggested that (1) our method has found feasible solutions with the same quality of those by human experts; (2) the solutions are globally better than those by the conventional reinforcement learning methods with regard to both the total wiring length and the number of iterations.
International conference proceedings, English

Fault tolerance in a multiple robots organization based on an organizational learning model
H Kasahara; K Takadama; S Nakasuka; K Shimohara
1998 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5, IEEE, 2261-2266, 1998, Peer-reviwed, This paper investigates the ability of reorganization in our organizational learning model to maintain the collective performance of multiple robots in terms of fault tolerance. In real applications using these robots, when the membership of robots is changed according to situation or some robots become defective or inoperative, it is necessary for those robots that remain to reform their organization in order to continue to complete given tasks. Through intensive simulations on the same truss construction task, the following experimental results were obtained: (1) Our model enables robots to continue to complete given tasks by reforming their organization, when a membership of robots is changed or some faulty robots are removed, and (2) The number of steps before operation does not increase very much as compared with the steps after operation.
International conference proceedings, English

Design for sequence of behavior rules in multiple robots reinforcement learning
K Takadama; K Hajiri; T Nomura; S Nakasuka; K Shimohara
INTELLIGENT AUTONOMOUS SYSTEMS, I O S PRESS, 327-334, 1998, Peer-reviwed, This paper proposes a novel method for storing a sequence of behavior rules in multiple robots reinforcement learning, and focuses on how a sequence of behavior rules works for getting out of deadlock situations and works for reducing both the steps and the iteration counts. Through intensive simulations of truss construction by multiple robots, the following experimental results have suggested: (1) Since the size of memory for storing a sequence of behavior rules is fixed in the proposed method, this method enables robots to acquire their own appropriate functions: (2) Robots with this method not only get out of deadlock situations but also complete given tasks both in few steps and few iteration counts.
International conference proceedings, English

Adaptation to multiple robots organization with organizational knowledge on formation
K Takadama; K Hajiri; T Nomura; S Nakasuka; K Shimohara
FROM ANIMALS TO ANIMATS 5, M I T PRESS, 483-488, 1998, Peer-reviwed, This paper extends our organizational learning model by introducing the mechanism of utilizing organizational knowledge to improve the collective performance, and focuses on how this organizational knowledge works for supporting new robots in the process of their adaptation to an organization of multiple robots. Through intensive simulations of truss construction by multiple robots, the following experimental results have suggested: (1) Robots with organizational knowledge complete given tasks in fewer iterations than those without organizational knowledge; (2) The steps with organizational knowledge become fewer as the number of robots increases; and (3) Organizational knowledge enables robots to complete given tasks which cannot be completed without organizational knowledge.
International conference proceedings, English

Organizational learning model for adaptive collective behaviors in multiple robots
K Takadama; K Hajiri; T Nomura; K Shimohara; S Nakasuka
ADVANCED ROBOTICS, TAYLOR & FRANCIS LTD, 12, 3, 243-269, 1998, Peer-reviwed, This paper proposes a novel organizational learning model in which multiple robots acquire their own functions for adaptive collective behaviors through local interactions among their neighbors and form an organizational structure to complete given tasks without global explicit control mechanisms or communication methods. In this paper, we focus on emergent processes in which robots dynamically form an organizational structure by acquiring their own appropriate functions to complete given tasks effectively and also focus on how organizational knowledge supports robots to reform their organizational structure. Through intensive simulations of truss construction by multiple robots, the following experimental results have suggested: (1) robots in our model acquire their own appropriate functions without global explicit control mechanisms or communication methods and form an organizational structure which completes given tasks in less steps than those with a centralized control system, and (2) organizational knowledge enables robots to complete the tasks which cannot be completed without it and contributes to reducing the steps for completing given tasks.
Scientific journal, English

Good solutions will emerge without a global objective function: Applying organizational-learning oriented classifier system to printed circuit board design
K Takadama; T Terano
SMC '97 CONFERENCE PROCEEDINGS - 1997 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5, I E E E, 3355-3360, 1997, Peer-reviwed, This paper describes a novel evolutionary computation model: Organizational-Learning Oriented Classifier System (OCS), and its application to Printed Circuit Boards (PCBs) design problems. The idea of OCS comes from the theory of Organizational learning in organizational sciences. OCS is an extended multiagent version of a conventional Learning Classifier System to learn adaptive rules in a given environment. OCS adaptively learns 'good' knowledge for problem solving via interaction among the agents without explicit control mechanisms nor a global optimization function. To validate the effectiveness of OCS, we have conducted intensive experiments on a real scale PCB design problem for electric appliances. The experimental results have suggested that (1) OCS has found feasible solutions with the same quality of the ones by human experts; (2) the solutions are not only locally optimal, but also globally better than the ones by human experts with regard to the total wiring length; and (3) the solutions are more preferable than the ones from the conventional Computer Aided Design (CAD) systems.
International conference proceedings, English

A computational group dialogue model with organizational learning
K Takadama; K Hajiri; T Nomura; M Okada; K Shimohara; S Nakasuka
1997 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT PROCESSING SYSTEMS, VOLS 1 & 2, IEEE, 174-179, 1997, Peer-reviwed, This paper proposes a computational group dialogue model with organizational learning in which the agents adapt to, the groups through communication. Recently, as dementia becomes one of serious social problems, it is required to apply the model which has a mechanism of adaptation to the groups to dementia patients, in order that patients hare chances to adapt to the groups through communication. In the simulations, the agents communicate with other agents in the groups and learn their own dialogue strategics for adaptation to the groups with establishing their own opinions through communication.
International conference proceedings, English

- Kido, T. and K. TakadAAAI 23 Spring Symposium Report on Socially Responsible AI for Well-Bing
Kido, T; K. Takadama
20 Jun. 2023, AI Magazine, 1-2

運転事故防止に向けた睡眠トータルケア：運転前の睡眠状況把握と運転後の睡眠改善
髙玉 圭樹
Lead, Feb. 2023, 自動車技術, 77, 2

オープンスペースディスカッション 2021 実施報告
能島 裕介; 高木 英行; 棟朝 雅晴; 濱田 直希; 西原 慧; 高玉 圭樹; 佐藤 寛之; 桐淵 大貴; 宮川 みなみ
This paper is a report on Open Space Discussion (OSD) held in Evolutionary Computation Symposium 2021. The purpose of OSD is to share and discuss problems at hand and future research targets related to evolutionary computation. Discussion topics are voluntarily proposed by some of the participants, and other participants freely choose one to join in the discussion. Through free discussions based on the open space technology framework, it is expected that participants will have new research ideas and start some collaborations. This paper gives the concept of OSD and introduces six topics discussed this year. This paper also shows the responses to the questionnaire on OSD for future discussions, collaborations, and related events., 進化計算学会, Jul. 2022, 進化計算学会論文誌，2022, 1, 1-9, Japanese, Peer-reviwed, Introduction scientific journal, 2185-7385

Reports of the AAAI 2019 Spring Symposium Series
Baldini, I; Barrett, C; Chella, A; Cinelli, C; Gamez, D; Gilpin, L; Hinkelmann, K; Holmes, D; Kido, T; Kocaoglu, M; Lawless, W; Lomuscio, A; Macbeth, J; Martin, A; Mittu, R; Patterson, E; Sofge, D; Tadepalli, P; Takadama, K; Wilson, S
30 Sep. 2019, AI Magazine, 40, 3, 59-66

Reports of the AAAI 2018 Spring Symposium Series,
Amato, C; Ammar, H.B; Churchill, E; Karpas, E; Kido, T; Kuniavsky, M; Lawless, W. F; Rossi, F; Oliehoek, F. A; Russell, S; Takadama, K; Srivastava, S; Tuyls, K; Allen, P. V; Venable, K. B; Vrancx, P; Zhang, S
Dec. 2018, AI magazine, 39, 4, 29-35, English, Peer-reviwed, Book review

Study of Analytical Methods on the Relationship between Sleep Quality and Stress with a focus on Human Circadian Rhythm.
Ryo Takano; Satoshi Hasegawa; Yuta Umenai; Takato Tatsumi; Keiki Takadama; Toru Shimuta; Toru Yabe; Hideo Matsumoto
AAAI Press, 2018, 2018 AAAI Spring Symposia, Stanford University, Palo Alto, California, USA, March 26-28, 2018., Peer-reviwed, conf/aaaiss/TakanoHUTTSYM18

XCSR based on compressed input by deep neural network for high dimensional data.
Kazuma Matsumoto; Ryo Takano; Takato Tatsumi; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
ACM, 2018, Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, Kyoto, Japan, July 15-19, 2018, 1418-1425, Peer-reviwed, conf/gecco/MatsumotoTTSKT18

XCS-CR: determining accuracy of classifier by its collective reward in action set toward environment with action noise.
Takato Tatsumi; Tim Kovacs; Keiki Takadama
ACM, 2018, Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, Kyoto, Japan, July 15-19, 2018, 1457-1464, Peer-reviwed, conf/gecco/TatsumiKT18

Classifier generalization for comprehensive classifiers subsumption in XCS.
Caili Zhang; Takato Tatsumi; Hiyoyuki Sato; Tim Kovacs; Keiki Takadama
ACM, 2018, Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, Kyoto, Japan, July 15-19, 2018, 1854-1861, Peer-reviwed, conf/gecco/ZhangTSKT18

Reports of the AAAI 2017 Spring Symposium Series,
Bohg, J; Boix, X; Chang, N; Churchill; E. Chu; V. Fang, F; Feldman, J; Gonzalez, A; Kido, T; Lawless, W; Montaña, J; ntanon, S; Sinapov, J; Sofge, D; Steels, L; eenson, M; Takadama, K; Yadav, A
Dec. 2017, AI magazine, Vol. 38, No. 4, 99-106, English, Peer-reviwed

Applying variance-based Learning Classifier System without Convergence of Reward Estimation into various Reward distribution
Takato Tatsumi; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
This paper focuses on a generalization of classifiers in noisy problems and aims at exploring learning classifier systems (LCSs) that can evolve accurately generalized classifiers as an optimal solution in several environments which include different type of noise. For this purpose, this paper employs XCS-CRE (XCS without Convergence of Reward Estimation) which can correctly identify classifiers as either accurate or inaccurate ones even in a noisy problem, and investigates its effectiveness in several noisy problems. Through intensive experiments of three LCSs (i.e., XCS as the conventional LCS, XCS-SAC (XCS with Self-adaptive Accuracy Criterion) as our previous LCS, and XCS-CRE) on the noisy 11-multiplexer problem where reward value changes according to (a) Gaussian distribution, (b) Cauchy distribution, or (c) Lognormal distribution, the following implications have been revealed: (1) the correct rate of the classifier of XCS-CRE and XCS-SAC converge to 100% in all three types of the reward distribution while that of XCS cannot reach 100%
(2) the population size of XCS-CRE is smallest followed by that of XCS-SAC and XCS
and (3) the percentage of the acquired optimal classifiers of XCS-CRE is highest followed by that of XCS-SAC and XCS., Institute of Electrical and Electronics Engineers Inc., 05 Jul. 2017, 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, 2017, CEC, 2630-2637, English, Peer-reviwed, 201702235307796961, 85027858488

Program Emergence Towards a Robust Space Computer System
高玉 圭樹; 原田 智広
Lead, システム制御情報学会, 23 May 2017, システム制御情報学会研究発表講演会講演論文集, 61, 6p, Japanese, 40021219730

Learning Classifier System Based on Variance of Reward for Clustering in Limited Number of Data: Application to Aged Care Plan
CHO ZAIRITU; TATSUMI TAKATO; TAKADAMA KEIKI
自動制御連合講演会, 2017, Proceedings of the Japan Joint Automatic Control Conference, 60, 0, 927-932, Japanese, 130006251472

Automatic adjustment of selection pressure based on range of reward in learning classifier system.
Takato Tatsumi; Hiroyuki Sato; Keiki Takadama
ACM, 2017, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, Berlin, Germany, July 15-19, 2017, 505-512, Peer-reviwed, conf/gecco/TatsumiST17

Well-being Computing : Towards Physical, Mental, and Social Well-being from Sleep Perspective
髙玉 圭樹; 村田 暁紀; 上野 史; 田島 友祐; 辰巳 嵩豊; 原田 智広
Lead, 人工知能学会 ; 2014-, Jan. 2017, 人工知能 : 人工知能学会誌, 32, 1, 81-86, Japanese, 2188-2266, 40021055972

特集 「Well-being Computing」にあたって
城戸 隆; 高玉 圭樹
人工知能学会, 01 Jan. 2017, 人工知能学会誌, 32, 1, 79-89, Japanese, Peer-reviwed, 190000000116

解釈性に優れた知識獲得技術としての進化的機械学習
中田 雅也; 高玉 圭樹
Last, 15 Jul. 2016, システム/制御/情報, Vol.60, No.7, 278-283, Japanese, Peer-reviwed

快眠を導く音とは：心拍・呼吸に連動した音の睡眠への影響
高玉 圭樹; 村田 暁紀; 上野 史; 田島 友祐; 原田 智広
Lead, 01 May 2016, 人工知能学会誌,, 31, 3, 383-388, Japanese, Peer-reviwed, 190000000150

Reports of the AAAI 2016 Spring Symposium Series
Amato, C; Amir, O; Bryson, J; Grosz, B; Indurkhya, B; Kiciman, E; Kido, T; W. F. Lawless; Liu, M; McDorman, B; Mead,R; F. A. Oliehoek; Specian, A; Stojanov, G; Takadama, K
Last, May 2016, AI magazine, 37, 4, 83-88, English, Peer-reviwed

Real-time sleep stage estimation from biological data with trigonometric function regression model
Tomohiro Harada; Fumito Uwano; Takahiro Komine; Yusuke Tajima; Takahiro Kawashima; Morito Morishima; Keiki Takadama
Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This paper proposes a novel method to estimate sleep stage in real-time with a non-contact device. The proposed method employs the trigonometric function regression model to estimate prospective heart rate from the partially obtained heart rate and calculates the sleep stage from the estimated heart rate. This paper conducts the subject experiment and it is revealed that the proposed method enables to estimate the sleep stage in realtime, in particular the proposed method has the equivalent estimation accuracy as the previous method that estimates the sleep stage according to the entire heart rate during sleeping., 01 Jan. 2016, AAAI Spring Symposium - Technical Report, SS-16-01 - 07, 348-353, 84980047676

特集「人の認知を拡張し健康を促進する環境知能」にあたって
城戸 隆; 髙玉 圭樹
01 Nov. 2015, 人工知能, 30, 6, 724-725, Japanese, 190000000053

生体リズムに連動した音と音色の違いが睡眠に及ぼす影響
山木 清志; 植屋 夕輝; 石原 淳; 森島 守人; 原田 智広; 高玉 圭樹; 角谷 寛
(一社)日本睡眠学会, Jul. 2015, 日本睡眠学会定期学術集会プログラム・抄録集, 40回, 255-255, Japanese, 2016017120

Handling Different Level of Unstable Reward Environment Through an Estimation of Reward Distribution in XCS
Takato Tatsumi; Takahiro Komine; Hiroyuki Sato; Keiki Takadama
XCS is an accuracy-based learning classifier system (LCS) which is powered by a reinforcement algorithm. We expect it will have when the reward for a state I action pair is unstable, because it is not possible to correctly estimate the evaluation. This paper focuses on learning in a different level of an unstable reward environment and proposes XCS-URE (XCS for Unstable Reward Environment) by improving XCS for such an environment. For this purpose, XCS-URE estimates the reward distribution of the classifier (i.e., if-then rule) by using the standard deviation of the acquired reward, and adjusts the accuracy of the classifier depending on the reward distribution. In order to investigate the effectiveness of XCS-URE, this paper applies XCS and XCS-URE into the multiple unstable reward environments which have a different level of the unstable rewards added by Gaussian noise. The experiments on the modified multiplexer problems have the following implications: (1) in the environment same Gaussian noise is added, XCS cannot performs properly due to the low accuracy of the classifier in the noisy environments, while XCS-URE can perform properly by acquiring the appropriate classifiers even in such an environment; (2) in the same environment, XCS-URE can reduce the population size without decreasing the correct rate as compared to XCS; and (3) even in the environment different Gaussian noises depending on the situation are added, XCS-URE can reduce the population size without decreasing the correct rate by adjusting the accuracy of the classifier depending on the reward distribution., IEEE, 2015, 2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2973-2980, English, Peer-reviwed, conf/cec/TatsumiKST15, WOS:000380444803002

コンシェルジュサービスに基づく介護支援システム：パーソナルヘルスデータからライフスタイル設計へ
高玉 圭樹
Lead, 人工知能学会 ; 2014-, 01 Nov. 2014, 人工知能学会誌, 29, 6, 585-590, Japanese, Peer-reviwed, Introduction scientific journal, 2188-2266, 110009865185, AA12652467

特集「Big Data Becomes Personal － 発見情報学が拓くヘルス& ウェルネス －」にあたって
城戸 隆; 高玉 圭樹
01 Nov. 2014, 人工知能学会誌, 29, 6, 580-582, Japanese, Peer-reviwed, Introduction scientific journal, 2188-2266, 201402232463780463

介護支援のための睡眠モニタリングエージェントとその展開
高玉 圭樹; 田島 友祐
Lead, 01 Oct. 2014, 電子情報通信学会，基礎境界のソサイエティ誌，Fundamentals Review, 8, 2, 96-101, Japanese, Peer-reviwed, Introduction scientific journal

パーソナルデータに基づく気づきの創発にあたって
城戸 隆; 高玉 圭樹
01 Nov. 2013, 人工知能学会誌, 28, 6, 827-828, Japanese, Peer-reviwed, Introduction scientific journal, 0912-8085, 110009675042, AN10067140

睡眠から高齢者の気持ちを察する次世代介護支援システム
高玉 圭樹
Lead, 人工知能学会, 01 Nov. 2013, 人工知能学会誌, 28, 6, 851-856, Japanese, Peer-reviwed, Introduction scientific journal, 0912-8085, 110009675046, AN10067140

Reports of the 2013 AAAI Spring Symposium Series
Vita Markman; Georgi Stojanov; Bipin Indurkhya; Takashi Kido; Keiki Takadama; George Konidaris; Eric Eaton; Naohiro Matsumura; Renate Fruchter; Don Sofge; William F. Lawless; Omid Madani; Rahul Sukthankar
The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext
Creativity and (Early) Cognitive Development
Data-Driven Wellness: From Self-Tracking to Behavior Change
Designing Intelligent Robots: Reintegrating AI II
Lifelong Machine Learning
Shikakeology: Designing Triggers for Behavior Change
Trust and Autonomous Systems
and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved., AI Access Foundation, 10 Oct. 2013, AI Magazine, 34, 3, 93-98, English, Introduction other, 0738-4602, 84896267515

新たな宇宙探査機を目指すローバの進化: ARLISSでの挑戦
高玉 圭樹; 杉本 悠太; 北川 広登
Lead, 計測自動制御学会, 10 Jun. 2013, 計測自動制御学会誌，特集号「ロボット競技と計測制御」, 52, 6, 515-521, Japanese, Introduction other, 0453-4662, 10031182149, AN00072406

Reports of the AAAI2012 Spring Symposia
Alani, H; An, B; Jain, M; Kido, T; Konidaris, G; Lawless, W; Martin, D; Pantofaru, C; Sofge, D; Takadama, K; Tambe, M; Vitvar, T
Sep. 2012, AI magazine, 33, 3, 109-114, English, Introduction other

Social and group simulation based on real data analysis
Kiyoshi Izumi; Keiki Takadama; Hiromitsu Hattori; Nariaki Nishino; Itsuki Noda
Recently, social simulation research based on real data has appeared in various fields. This paper introduces studies of Agent-Based Simulation (ABSs) based on real data, focusing on introducing studies in the fields of financial marketing, traffic, pedestrians, and a sustainable society. We also introduce some approaches to establish a general method and/or theory about linking social simulation to real data. Finally, we categorize ABS research for understanding ABS research features., Fuji Technology Press, Mar. 2011, Journal of Advanced Computational Intelligence and Intelligent Informatics, 15, 2, 166-172, English, Peer-reviwed, Introduction other, 1883-8014, 79952925138

プログラム進化を促進する人工衛星の制御基板
高玉 圭樹
Lead, Jan. 2011, 精密工学学会誌, 77, 1, 46-50, Japanese, Peer-reviwed, Introduction other

Analysis on Transport Networks of Railway, Subway and Waterbus in Japan From the Viewpoint of Complex Network(Summaries of Papers Published by Staff of National Maritime Research Institute at Outside Organizations) :
間島 隆博; 高玉 圭樹; 勝原 光治郎
海上技術安全研究所, Mar. 2009, 海上技術安全研究所報告, 8, 4, 435-435, Japanese, 1346-5066, 110007661658, AA11566574

緊急時の代替輸送支援システム マルチエージェントシステムと物流解析への応用
間島隆博; 高玉圭樹
Last, 29 Mar. 2008, 海上技術安全研究所報告, 7, 4, 493-497, Japanese, 1346-5066, 200902220063756964

Progress in Model-To-Model Analysis
Juliette Rouchier; Claudio Cioffi-Revilla; J. Gary Polhill; Keiki Takadama
Last, J A S S S, Mar. 2008, THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, 11, 2, English, Peer-reviwed, Introduction other, 1460-7425, WOS:000265355000008

Network Route Design of Public Transport System with Network Evolution(Summaries of Papers Published by Staff of National Maritime Research Institute at Outside Organizations) :
間島 隆博; 高玉 圭樹; 渡部 大輔; 勝原 光治郎
海上技術安全研究所, Jun. 2007, 海上技術安全研究所報告, 7, 1, 125-125, Japanese, 1346-5066, 110007661321, AA11566574

Analysis on Transport Networks of Railway, Subway and Waterbus in Japan(Summaries of Papers Published by Staff of National Maritime Research Institute at Outside Organizations)
間島 隆博; 勝原 光治郎; 高玉 圭樹
Last, 海上技術安全研究所, Sep. 2006, 海上技術安全研究所報告, 6, 2, 234-234, Japanese, 1346-5066, 110007661194, AA11566574

相互作用の本質に迫る
高玉 圭樹
Lead, Dec. 2005, 「相互作用の本質に迫る: 知的システムの理解と設計の新視点」特集号，計測自動制御学会，計測と制御, 44, 12, 817-818, Japanese, Introduction other, 10016933404

相互作用の設計原理を目指して
高玉 圭樹
Lead, Dec. 2005, 「相互作用の本質に迫る: 知的システムの理解と設計の新視点」特集号，計測自動制御学会，計測と制御，, 44, 12, 831-835, Japanese, Introduction other

Relationship between Knowledge Diversity and Available Information in Multi-agent Systems
Inoue Hiroyasu; Takadama Keiki; Shimohara Katsunori; Katai Osamu
Forum on Information Technology, 20 Aug. 2004, 情報科学技術フォーラム一般講演, 3, 2, 313-316, Japanese, 110007683840, AA11740605

Exploring Interaction among Agents, Robots, and Human, The Second Organized Session
TAKADAMA K.
Aug. 2003, SICE Annual Conference 2003, 10014869081

Exploring Interaction among Agents, Robots, and Human, The First Organized Session
TAKADAMA K.
2002, The Forth Asia-Pacific Conference on Simulated Evolution And Learning (SEAL'02), 10014869080

Designing Multiple Agents using Learning Classifier Systems
TAKADAMA K.
Corresponding, Nov. 2000, The Fourth Japan-Australia Joint Workshop on Intelligent and Evolutionary Systems (JA'00), 10014869075

Can Multiagents Learn in Organization? 〜Analyzing Organizational-Learning Oriented Classifier System
K. Takadama; T. Terano; K. Shimohara; K. Hori; S. Nakasuka
Lead, Nov. 1999, The 16th International Joint Conference on Artificial Intelligence (IJCAI'99) Workshop on Agents Learning About, From and With other Agents, 10004577506

Function Acquisition of Learning Multiple Agents
TAKADAMA Keiki; TERANO Takao; SHIMOHARA Katsunori
Lead, 人工知能学会, 15 Jun. 1999, 人工知能学会全国大会論文集 = Proceedings of the Annual Conference of JSAI, 13, 290-293, Japanese, 0914-4293, 10009927613

Analyzing the roles of problem solving and learning in organizational-learning oriented classifier system
K Takadama; S Nakasuka; T Terano
Lead, This paper analyzes the roles of problem solving and learning in Organizational-learning oriented Classifier System (OCS) from the viewpoint of organizational learning in organization and management sciences, and validates the effectiveness of the roles through the experiments of large scale problems for Printed Circuit Boards (PCBs) re-design in the Computer Aided Design (CAD). OCS is a novel multiagent-based architecture, and is composed of the following four mechanisms: (1) reinforcement learning, (2) rule generation, (3) rule exchange, and (4) organizational knowledge utilization. In this paper, we discuss that the four mechanisms in OCS work respectively as an individual performance/concept learning and an organizational performance/concept learning in organization and management sciences. Through the intensive experiments on the re-design problems of real scale PCBs, the results suggested that four learning mechanisms in individual/organizational levels contribute to finding not only feasible part placements in fewer iterations brit also the shorter total wiring length than the one by human experts., SPRINGER-VERLAG BERLIN, Nov. 1998, PRICAI'98: TOPICS IN ARTIFICIAL INTELLIGENCE, 1531, 71-82, English, 0302-9743, WOS:000084730100007

Reinforcement learning for multiple robots with organizational learning
TAKADAMA K.
Corresponding, Sep. 1998, Proc. 3rd Int. Symp. on Artificial Life and Robotics (AROB '98), 392-396, 10010976264

Organizational Learning for Adaptive Behaviors as an Organized Group of Swarm Robots
TAKADAMA Keiki; HAJIRI Koichiro; NOMURA Tatsuya; NAKASUKA Shinichi; SHIMOHARA Katsunori
Lead, 18 Mar. 1997, 計測自動制御学会, 知能システムシンポジウム資料, 24, 23-28, Japanese, 10016720011

A Dialogue Strategy for an Adaptive Behavior as a Group
TAKADAMA Keiko; HAJIRI Koichiro; OKADA Michio; SHIMOHARA Katsunori
Lead, The relationship that is made by dialogue effects to the next coming dialogue. A new relationship is made as a result of the new dialogue, and under the new relationship, the dialogue continues. In this way, there is a kind of cycle in the dialogue. This is very similar to emergence, that is the central concept in Artificial Life study. In other saying, even in the dialogue study, "the behavior of a group is not a simple sum of individual behaviors" To study about such "group dialogue", we have to take an approach that considers the dynamics of the group, not just attaching or extending the technology that is developed in normal dialogue study. In this study, we simulate the emergence of individual strategy for the dialogue, and the self-organization of the group. In this way, we show a direction of group dialogue study that is not discussed yet, as a milestone., Information Processing Society of Japan (IPSJ), 26 Jul. 1996, IPSJ SIG Notes, 1996, 74, 83-88, Japanese, 0919-6072, 110002916917, AN10442647

Reinforcement Learning in Cyclic Environmental Change for Non-Communicative Agents: A Theoretical Approach, Explainable and Transparent AI and Multi-Agent Systems
Uwano, F; Takadama, K
Joint work, 143-159, Lecture Notes in Computer Science, Vol. 14127, Springer-Verlag, 04 Sep. 2023, Peer-reviwed

Personalized Sleep Stage Estimation based on Time Series Probability of Estimation for Each Label with Wearable 3-axis Accelerometer
Nakari, I; Nakashima, M; TAKADAMA, K
Joint work, 531-542, Human Interface and the Management of Information, in H. Mori and Y. Asahi (eds.), Lecture Notes in Computer Science, Springer-Verlag, 28 Jul. 2023, Peer-reviwed, The 25th International Conference on Human-Computer Interaction (HCI International
2023)

Multi-objectivization Relaxes Multi-funnel Structures in Single-objective NK-landscapes
Tanaka, S; Takadama, K; Sato, H
Joint work, 195-210, Evolutionary Computation in Combinatorial Optimisation (EvoCOP2023), Lecture Notes in Computer Science, Vol. 13987, Springer Nature, Apr. 2023, Peer-reviwed

Adaptive Synapse Adjustment and De- coding in Action-prediction Cortical Learning Algorithm
Fujino, K; Aoki, T; Takadama, K; Sato, H
811-821, Soft Computing and Pattern Recognition (SoCPaR 2022) , Lecture Notes in Networks and Systems, Vol. 648, Springer Nature, 28 Mar. 2023, Peer-reviwed

Pareto Front Upconvert by Iterative Estimation Modeling and Solution Sampling
Takagi, T; Takadama, K; Sato, H
Joint work, 218-230, Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, 23 Mar. 2023, Peer-reviwed, Vol. 13970

Design of Human-Agent-Group Interaction for Correct Opinion Sharing on Social Media
Uwano, F; Yamane, D; Takadama
Joint work, 146-165, Human Interface and the Management of Information, Part Ⅰ, Lecture Notes in Computer Science, Vol.13305, 28 Jun. 2022, Peer-reviwed

Inheritance vs. Expansion: Generalization Degree of Nearest Neighbor Rule in Continuous Space as Covering Operator of XCS
Hiroki Shiraishi; Yohei Hayamizu; Iko Nakari; Hiroyuki Sato; Keiki Takadama
Applications of Evolutionary Computation, Lecture Notes in Computer Science, Vol. 13224, 15 Apr. 2022, Peer-reviwed

Formalizing Model-based Multi-Objective Reinforcement Learning with a Reward Occurrence Probability Vector
Yamaguchi, T; Kawabuchi, Y; Takahashi, S; Ichikawa, Y; Takadama, K
English, Joint work, 299-330, Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning, IGI Global, Jan. 2022, Peer-reviwed, chap.12

Analyzing Early Stage of Forming a Consensus from Viewpoint of Majority/Minority Decision in Online-Barnga
Maekawa, Y; Tomohiro, Y; Takadama, K
English, Joint work, 269-285, Human Interface and the Management of Information, Part Ⅱ Lecture Notes in Computer Science, Vol. 12766, Springer-Verlag, 29 Jul. 2021, Peer-reviwed, vol.12766

Generating Duplex Routes for Robust Bus Transport Network by Improved Multi-objective Evolutionary Algorithm Based on Decomposition
Kajihara, S; Sato, H; Takadama, K
English, Joint work, 65-80, Applications of Evolutionary Computation, Vol. 12694 Lecture Notes in Computer Science, Springer-Verlag, 09 Apr. 2021, Peer-reviwed, vol.12694

Pareto Front Estimation Using Distance from Unit Hyperplane, Evolutionary Multi-Criterion Optimization
Takagi, T; Takadama, K; Sato, H
English, Joint work, 126-138, Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, Vol. 12654, Springer-Verlag, 28 Mar. 2021, Peer-reviwed, vol.12654

Towards Agent Design for Forming a Consensus Remotely Through an Analysis of Declaration of Intent in Barnga Game
Maekawa, Y; Yamaguchi, T; Takadama, K
English, Joint work, 540-546, Advances in Intelligent Systems and Computing (AISC), Vol.1322, Springer, 23 Feb. 2021, Peer-reviwed, vol.1322

How to Emote for Consensus Building in Virtual Communication Virtual Communication
Maekawa, Y; Uwano, F; Kitajima, E; Takadama, K
English, Joint work, 194-205, Human Interface and the Management of Information, Lecture Notes in Computer Science, Vol. 12185, Part II, Springer-Verlag, 21 Jul. 2020, Peer-reviwed, vol.12185, part. Ⅱ

Simultaneous Local Adaptation for Different Local Properties
Kobayashi, R; Takano, R; Sato, H; Takadama, K
English, Joint work, 216-227, Adaptation, Learning and Optimization, Vol. 12 Springer-Verlag, 08 Dec. 2019, Peer-reviwed, vol.12

Model-Based Multi-Objective Reinforcement Learning by a Reward Occurrence Probability Vector
Yamaguchi, T; Nagahama, S; Ichikawa, Y; Honma, Y; Takadama, K
English, Joint work, 269-296, Advanced Robotics and Intelligent Automation in Manufacturing IGI Global, Nov. 2019, Peer-reviwed

How to Design Adaptable Agents to Obtain Consensus with Omoiyari
Maekawa, Y; Uwano, F; Kitajima, E; Takadama, K
Scholarly book, English, Joint work, 462-475, Human Interface and the Management of Information, Lecture Notes in Computer Science, Vol. 11569, Part Ⅰ, Springer-Verlag, 30 Jul. 2019, Peer-reviwed, vol.11569, part . I

Model-based Multi-Objective Reinforcement Learning with Unknown Weights
Yamaguchi, T; Nagahama, S; Ichikawa, Y; Takadama, K
Scholarly book, English, Joint work, 311-321, Human Performance Technology: Concepts, Methodologies, Tools, and Applications, Chapter 10 IGI Global, May 2019, Peer-reviwed, vol.11570, part. Ⅱ

A Distribution Control of Weight Vector Set for Multi-objective Evolutionary Algorithms
Takagi, T; Takadama,K; Sato, H
Scholarly book, English, Joint work, 70-80, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering(LNICST), Springer-Verlag, 13 Mar. 2019, Peer-reviwed, vol.289

Evolving Generalized Solutions for Robust Multi-Objective Optimization: Transportation Analysis in Disaster
Takadama K; Sato, K; Sato, H
Scholarly book, English, Joint work, 491-503, Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, Vol. 11411, Springer-Verlag, 12 Mar. 2019, Peer-reviwed, vol. 11411

Strategy for Learning Cooperative Behavior with Local Information for Multi-agent Systems
Uwano, F; Takadama, K
Scholarly book, English, Joint work, 663-670, Principles and Practice of Multi-Agent Systems, Lecture Notes in Computer Science, Vol. 11224,Springer-Verlag, 01 Nov. 2018, Peer-reviwed, vol.11224

Awareness Based Recommendation by Passively Interactive Learning: Toward a Probabilistic Event
Yamaguchi, T; Nishimura, T; Nagahama, S; Takadama, K
Scholarly book, English, Joint work, 247-275, Novel Design and Applications of Robotics Technologies, Chapter 9 IGI Global, Sep. 2018, Peer-reviwed, chap.9

Correcting Wrongly Determined Opinions of Agents in Opinion Sharing Model
Kitajima, E; Zhang, C. Ishii, H; Uwano, F; Takadama, K
Scholarly book, English, Joint work, 658-676, Human Interface and the Management of Information, Lecture Notes in Computer Science, Vol. 10904, Springer-Verlag, 19 Jul. 2018, Peer-reviwed

Generating Learning Environments Derived from Found Solutions by Adding Sub-goals toward the Creative Learning Support
Okudo, T; Yamaguchi, T; Takadama, K
Scholarly book, English, Joint work, 313-330, Human Interface and the Management of Information, Lecture Notes in Computer Science, Vol. 10905, Springer-Verlag, 19 Jul. 2018, Peer-reviwed, vol. 10905

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference
Aguirre, H; Takadama, K
Scholarly book, English, Joint work, ISBN: 978-1-4503-5618-3, ACM, 15 Jul. 2018

Proceedings of the Genetic and Evolutionary Computation Conference Companion
Aguirre, H; Takadama, K
Joint work, ACM, Jul. 2018

Analyzing the goal findingprocess of human's learning with the reflection subtask
Yamaguchi; T. Tamai, Y; Takadama, K
Scholarly book, English, Joint work, 442-459, Handbook of Research on Biomimetics and Biomedical Robotics, Chapter 19 IGI Global, Dec. 2017, Peer-reviwed, chap.19

Strategy to Improve Cuckoo Search toward Adapting Randomly Changing Environment
Umenai, Y; Uwano, F; Sato, H; Takadama, K
Scholarly book, English, Joint work, 573-582, Advances in Swarm Intelligence, Lecture Notes in Computer Science, Vol. 10385, Springer-Verlag, 27 Jul. 2017, Peer-reviwed

Towards Adaptive Aircraft Landing Order with Aircraft Routes Partially Fixed by Air Traffic controllers as Human Intervention
Murata, A; Sato, H; Takadama, K
Scholarly book, English, Joint work, 422-433, Human Interface and the Management of Information, Part II, Lecture Notes in Computer Science, Vol. 10274, Springer-Verlag, 12 Jul. 2017, Peer-reviwed

Designing the learning goal space for human toward acquiring a creative learning skill
Okudo, T; Takadama, K; Yamaguchi, T
Scholarly book, English, Joint work, Human Interface and the Management of Information, Part II, Lecture Notes in Computer Science, Vol. 10274, 460-475, Springer-Verlag, 12 Jul. 2017, Peer-reviwed

Generating Hub-Spoke Network for Public Transportation: Comparison between Genetic Algorithm and Cuckoo Search Algorithm
Majima, T; Takadama, K; Watanabe, D; Katsuhara, M
Scholarly book, English, Joint work, 263-275, Intelligent and Evolutionary System, Springer-Verlag, 17 Nov. 2016, Peer-reviwed

Optimization of Aircraft Landing Route and Order Based on Novelty Search
Murata, A; Sato, H; Takadama, K
Scholarly book, English, Joint work, 291-304, Intelligent and Evolutionary System, Springer-Verlag, 17 Nov. 2016, Peer-reviwed

Communication-less Cooperative Q-learning Agents in Maze Problem
Uwano, F; Takadama, K
Scholarly book, English, Joint work, 453-467, Intelligent and Evolutionary System, Springer--Verlag, 17 Nov. 2016, Peer-reviwed

Preventing Incorrect Opinion Sharing with Weighted Relationship among Agents
Saito, R; Nakata, M; Sato, H; Kovacs, T; Takadama, K
Scholarly book, English, Joint work, 50-62, The 18th International Conference on Human-Computer Interraction (HCI 2016), Lecture Notes in Computer Science, Vol. 9735, Part II, Springer-Verlag, 20 Jul. 2016, Peer-reviwed

Personalized real-time sleep stage from past sleep data to today's sleep stage estimation
Tajima, Y; Harada, T; Sato, H; Takadama, K
Scholarly book, English, Joint work, 501-510, The 18th International Conference on Human-Computer Interraction (HCI 2016), Lecture Notes in Computer Science, Vol. 9735, HCI-2016: Part II Springer-Verlag, 20 Jul. 2016, Peer-reviwed

Awareness Based Recommendation Toward A New Preference: Evaluation of The Awareness Effect
Yamaguchi, T; Nishimura, T; Takadama, K
English, Joint work, Chapter 9, 219-241, Handbook of Research on Advancements in Robotics and Mechatronics, IGI Global, 05 Apr. 2015

Messy Coding in the XCS Classifier System for Sequence Labeling
Nakata, M; Kovacs, T; Takadama, K
English, Joint work, Vol8672, 191-200, Lecture Notes in Computer Science, Springer-Verlag, 17 Sep. 2014, Peer-reviwed, vol.8672

Visualizing mental learning processes with invisible mazes for continuous learning
Yamaguchi, T; Takemori, K; Takadama, K
Scholarly book, English, Joint work, Vol. 8522, 137-148, Lecture Notes in Computer Science, Springer-Verlag, 25 Jun. 2014, Peer-reviwed, vol.8522

Favor Information Presentation and its Effect for Collective-Adaptive Situation
Mori, A; Harada, T; Ichikawa, Y; Takadama, K
Scholarly book, English, Joint work, Vol. 8522, 455-466, Lecture Notes in Computer Science, Springer-Verlag, 22 Jun. 2014, Peer-reviwed

Asynchronous Evolution by Reference-based Evaluation: Tertiary Parent Selection and its Archive
Harada, T; Takadama, K
Scholarly book, English, Joint work, Vol. 8599, 198-209, Lecture Notes in Computer Science, Springer-Verlag, 23 Apr. 2014, Peer-reviwed

Controlling Selection Area of Useful Infeasible Solutions in Directed Mating for Evolutionary Constrained Multiobjective Optimization
Miyakawa, M; Takadama, K; Sato, H
Scholarly book, English, Joint work, vol.8426, 137-152, Lecture Notes in Computer Science, Springer-Verlag, 20 Feb. 2014, Peer-reviwed

What is Needed to Promote an Asynchronous Program Evolution in Genetic Programing?
Takadama, K; Harada, T; Sato H; Hattori, K
Scholarly book, English, Joint work, Vol.8426, 227-241, Lecture Notes in Computer Science, Springer-Verlag, 20 Feb. 2014, Peer-reviwed

Towards Understanding of Pareto Solutions in Multi-Dimensional Space via Interactive System
Takadama, K; Sawadaishi, Y; Harada, T; Ichikawa, Y; Sato, k; Hattori, H; Yamaguchi, T
English, Joint work, The 15th International Conference on Human-Computer Interaction, Vol. 8018, 137-146, Lecture Notes in Computer science, Springer-Verlag, Jul. 2013, Peer-reviwed

Modeling a human's learning and mastery process toward learning support system on human computer interaction
Takemori, K; Yamaguchi, T; Sasaji, K; Takadama, K
English, Joint work, The 15th International Conference on Human-Computer Interaction Vol. 8016, 555-564, Lecture Notes in Computer Science, Springer-Verlag, 2013, Peer-reviwed, vol.8016

Modeling a Human's Learning Processese Toward Continuous Learning Support System
Yamaguchi, T; Takemori, K; Takadama, K
English, Joint work, 69-94, Mechatronics Engineering, Wiley-ISTE, 2013

Awareness Based Recomendation- Toward The Human Adaptive and Friendly Interactive learning System Engineerning Creative Design in Robotics and Mechanics
Yamaguchi, T; Nishimura, T; Takadama, K
English, Joint work, Chapter 6, 86-100, Engineering Creative Design in Robotics and Mechatronics, IGI Global, 2013

Asynchronous Evaluation based Genetic Programming : Comparison of Asynchronous Synchronous Evaluation andits Analysis," 16th European Conference on Genetic Programming, Lecture Notes in Computer Science
Harada, T; Takadama, K
English, Joint work, Vol. 7831, 241-252, Lecture Notes in Computer Science, Springer-Verlag, 2013, Peer-reviwed

XCS with Adaptive Action Mapping: Ninth International Conference on Simulated Evolution and Learning (SEAL2012))
Nakata, M; Lanzi, P. L; Takadama, K
English, Joint work, Vol. 7673, 138-147, Lecture Notes in Computer Science, Springer-Verlag, 2012, Peer-reviwed

Enhancing Learning Capabilities by XCS with Best Action Mapping Parallel Problem Solving from Nature PPSN XII
Nakata, M; Lanzi, P. L; Takadama, K
English, Joint work, Vol. 7491, 256-265, Lecture Notes in Computer Science, Springer-Verlag, 2012, Peer-reviwed

Bayesian Analysis Method of Time Series Data in Greenhouse Gas Emissions Trading Market
Nakata, T; Takadama, K; Watanabe, S
English, Joint work, Post Proceedings of the 6th international Workshop on Agent-based Approaches in Economic and Social Complex Systems(AESCS-2009), AESCS-2009, Springer-Verlag, Jul. 2011

What Kinds of Human Negotiation Skill Can be Acquired by Changing Negotiation Order of Bargaining Agents?
Takadama, K; Otaki, A; Sato, K; Matsushima, H; Otani, M; Ichikawa, Y; Hattori, K; Sato, H
English, Joint work, Vol. 6772, Lecture Notes in Computer Science, Springer-Verlag, 2011

Lecture Notes in Computer Science, Vol. 6772
Takadama, K; Otaki, A; Sato, K; Matsushima, H; Otani, M; Ichikawa, Y; Hattori, K; Sato, H
English, Joint work, What Kinds of Human Negotiation Skill Can be Acquired by Changing Negotiation Order of Bargaining Agents?, Springer--Verlag, 2011

Post Proceedings of The Sixth International Workshop onAgent-based Approaches in Economic and Social Complex Systems (AESCS'09)
Nakada, T; Takadama, K; Watanabe, S
English, Joint work, Bayesian Analysis Method of Time Series Data in Greenhouse Gas Emissions Trading Market,, Springer-Verlag Tokyo, 2010

Springer Series on Agent Based Social Systems (ABSS) Vol. 7
Takadama, K; Cioffi-Revilla, C; Deffuant, G
English, Joint work, Simulating Interacting Agents and Social Phenomena: The Second World Congress, Springer-Verlag, 2010

Parallel Problem Solving from Nature PPSN XI Lecture Notes in Computer Science
Shimada, T; Otani, M; Matsushima, H; Sato, H; Hattori; K; Takadama, K
English, Joint work, `Hybrid Directional-biased Evolutionary Algorithm for Multi-objective Optimization,, Springer-Verlag, 2010

Advances in Neural Network Research and Applications, Lecture Note in Electrical Engineering, Vol. 67
Otani, A; Hattori, K; Takadama, K
English, Joint work, Large-Scale Structure Assembly by Multiple Robots Which May be broken,, Springer-Verlag, 2010

自己組織化ハンドブック (国武 豊喜 (監修))
高玉 圭樹
Japanese, Joint work, 組織学習, NTS出版, 2009

AI 2009: Advances in Artificial Intelligence -The 22nd Australasian Joint Conference LNAI 5866
Ushida, Y; Hattori, K; Takadama, K
English, Joint work, From My Agent to Our Agent: Exploring Collective Adaptive Agent via Barnga, Springer, 2009

Operations Research and Its Applications: The 8th InternationalSymposium on Operations Research and Its Applications (ISORA 2009),Lecture Notes in Operations Research 10
Watanabe, D; Majima, T; Takadama, K; Katsuhara, M
English, Joint work, Generalized Weber Model for Hub Location of Air Cargo,, World Publishing Corporation, 2009

10th International Workshop, IWLCS 2006 and 11th International Workshop, IWLCS 2007,Revised Selected, Lecture Notes in Computer Science
Bacardit, J; Bernado-Mansilla, E; Butz, M.V; Kovacs, T; Llora, X; Takadama, K
English, Editor, Learning Classifier Systems, Vol. 4998, Springer-Verlag, 2008

Artificial Intelligence-A modern Approach (Second Edition)
高玉 圭樹; 古川康一; 中島秀行; 鷲尾隆; 犬塚信博; 新田克己
Japanese, Single translation, ``実世界におけるプランニングと行為''，12章 翻訳, 共立出版, 2008

Learning Classifier Systems: Workshops, IWLCS 2003-2005, Revised Selected Papers,Lecture Notes in Artificial Intelligence, Vol. 4399
Wada, A; Takadama, K; Shimohara, K
English, Joint work, Counter Example for Q-Bucket-Brigade under Prediction Problem, Springer-Verlag, 2007

Learning Classifier Systems: Workshops, IWLCS 2003-2005, Revised Selected Papers, Lecture Notes in Artificial Intelligence Vol. 4399
English, Editor, Springer-Verlag, 2007

Proceedings of the Third International Model-to-Model Workshop (M2M'07)
Rouchier; J. Cioffi-Revilla; C. Polhill, G; Takadama K
English, Editor, GREQAM-CNRS, 2007

Analysis on Transport Networks of Railway, Subway and Waterbus in Japan" in Emergent Intelligence of Networked Agents,Studies in Computational Intelligence, Vol. 56
Majima, T; Katsuhara, M; Takadama, K
English, Joint work, Springer-Verlag, 2007

A Partitioned Random Network Agent Model for Organizational Sectionalism Studies" in New Frontiers in Artificial Intelligence - JSAI 2003 and JSAI2004 Conference and Workshops, Lecture Notes in Artificial Intelligence, Vol. 3609
Yuta, K; Fujiwara, Y; Souma, W; Takadama, K; Shimohara, K; Katai, O
English, Joint work, Springer-Verlag, 2007

Analyzing Parameter Sensitivity and Classifier Representations for Real-valued XCS" Learning Classifier Systems: Workshops, IWLCS 2003-2005, Revised Selected Papers, Lecture Notes in Artificial Intelligence, Vol. 4399
Wada, A; Takadama, K; Shimohara, K; Katai, O
English, Joint work, Springer-Verlag, 2007

Counter Example for Q-Bucket-Brigade under Prediction Problem"in Learning Classifier Systems: Workshops, IWLCS 2003-2005, Revised Selected Papers,Lecture Notes in Artificial Intelligence, Vol. 4399
Wada, A; Takadama, K; Shimohara, K
English, Joint work, Springer-Verlag, 2007

X-MAS: Validation tool based on meta-programming, Agent-based Approaches in Economic and Social Complex Systems IV:Post-proceedings of The Forth AESCS Interaction Workshop 2005(AESCS'05)
Suematsu, Y. L; Takadama, K; Shimohara, K; Katai, O
English, Joint work, Springer-Verlag, 2007

Multi-Agent-Based Simulation VII (MABS'06)Lecture Notes in Computer Science Vol. 4442
Antunes, L; Takadama K
English, Editor, Springer-Verlag, 2007

Exploring Quantitative Evaluation Criteria for Service and Potentials of New Service in Transportation: Analyzing Transport Networks of Railway, Subway, and Waterbus ,'' The Eighth International Conference on Intelligent Data Engineering and Automated L
Takadama, K; Majima, T; Watanabe, D; Katsuhara, M
English, Joint work, Springer-Verlag, 2007

Emergent Intelligence of Networked Agents, Studies in Computational Intelligence,Vol. 56
Majima, T; Katsuhara, M; Takadama, K
English, Joint work, Analysis on Transport Networks of Railway, Subway and Waterbus in Japan, Springer-Verlag, 2007

New Frontiers in Artificial Intelligence - JSAI 2003 and JSAI2004 Conference and Workshops, Lecture Notes in Artificial Intelligence,Vol. 3609
Yuta, K; Fujiwara, Y; Souma, W; Takadama, K; Shimohara, K; Katai, O
English, Joint work, A Partitioned Random Network Agent Model for Organizational Sectionalism Studies, Springer-Verlag, 2007

Learning Classifier Systems: Workshops, IWLCS 2003-2005, Revised Selected Papers,Lecture Notes in Artificial Intelligence, Vol. 4399
Wada, A; Takadama, K; Shimohara, K; Katai, O
English, Joint work, Analyzing Parameter Sensitivity and Classifier Representations for for Real-valued XCS, Springer-Verlag, 2007

Agent-based Approaches in Economic and Social Complex Systems IV:Post-proceedings of The Forth AESCS Interaction Workshop 2005(AESCS'05)
Suematsu, Y. L; Takadama, K; Shimohara, K; Katai, O
English, Joint work, X-MAS: Validation tool based on meta-programming, Springer-Verlag, 2007

Multi-Agent-Based Simulation VII (MABS'06), Lecture Notes in Computer Science, Vol. 4442
Takadama, K; Kawai, T; Koyama, Y
English, Joint work, Can Agents Acquire Human-like Behaviors in a Sequential Bargaining Game? - Comparison of Roth's and Q-learning agents -, Springer-Verlag, 2007

A Partitioned Random Network Agent Model for Organizational Sectionalism Studies, JSAI 2003/2004 Workshop Post-Proceedings, Lecture Notes in Computer Science
Yuta, K; Fujiwara, Y; Souma, W; Takadama, K; Shimohara, K; Katai, O
English, Joint work, Springer-Verlag, 2006

Analyzing BARNGA Gaming Simulation through Modelling an Agent-Based Model,'' Agent Based Modeling Meets Gaming Simulation 2003
Suematsu, Y. L; Takadama, K; Shimohara, K; Katai, O; Arai, K
English, Joint work, Springer-Verlag, 2006

Detecting Failure of Spacecraft Using Separated States in Particle Filters,''Post-proceeding of The 25th International Symposium on Space Technology and Science: ISTS'06
Takadama, K; Murakami, T; Kawahara, Y
English, Joint work, Nissei eblo Inc., 2006

Multi-Agent-Based Simulation IV, The First Joint Workshop on Multi-Agent and Multi-Agent-Based Simulation (MAMABS'04), Lecture Notes in Computer Science
Takadama, K; Fujita, H
English, Joint work, Toward Guidelines for Modeling Learning Agents in Multiagent-Based Simulation: Implications from Q-learning and Sarsa agents, Springer-Verlag,, 2005

Agent-Based Simulation: From Modeling Methodologies to Real-World Applications,Post-proceeding of The Third International Workshop on Agent-based Approaches in Economic and Social Complex Systems (AESCS'04)
Suematsu, Y. L; Takadama, K; Shimohara, K; Katai, O; Arai, K
English, Joint work, Evaluation Criteria for Learning Mechanisms Applied to Agents in a Cross-Cultural Simulation, Springer-Verlag, 2005

Multi-Agent-Based Simulation IV, The First Joint Workshop on Multi-Agent and Multi-Agent-Based Simulation (MAMABS'04), Lecture Notes in Computer Science
Davidsson, P; Logan, B; Takadama K
English, Editor, Springer-Verlag, 2005

Foundations of Learning Classifier Systems
Wada, A; Takadama, K; Shimohara, K; Katai, O
English, Joint work, Learning Classifier Systems with Convergence and Generalization, Springer-Verlag, 2005

Genetic and Evolutionary Computation Conference (GECCO 2005) Workshop
Rothlauf, F; Blowers, M; Branke, J; Cagnoni, S; Garibay,I.I; Garibay, O; Grahl; J. Hornby, G; de Jong, E.D; Kovacs, T; Kumar, S; Lima, C.F; Llora, X; Lobo, F; Merkle, L.D; Miller,J; Moore, J.H; O'Neill, M; Pelikan, M; Riopka, T.P; Ritchie,M.D; Sastry, K; Smith, S.L; Stringer, H; Takadama, K; Toussaint, M; Upton, S.C; Wright, A.H; Yang, S
English, Editor, ACM Press, 2005

Agent Based Modeling Meets Gaming Simulation 2003
Suematsu, Y. L; Takadama, K; Shimohara, K; Katai, O; Arai, K
English, Joint work, Analyzing BARNGA Gaming Simulation through Modelling an Agent-Based Model, Springer-Verlag, 2005

Applications of Learning Classifier Systems, Fuzziness and Soft Computing series
Takadama, K
English, Joint work, Exploring Organizational-Learning Oriented Classifier System in Real-World Problems, Springer-Verlag, 2004

Recent Advances in Simulated Evolution and Learning World Scientific
Wada, A; Takadama K; Shimohara, K; Katai, O
English, Joint work, Autonomous Symbol Acquisition Through Agent Communication, 2004

Meeting the Challenge of Social Problems via Agent-Based Simulation: Post Proceedings of The Second International Workshop on Agent-based Approaches in Economic and Social Complex Systems (AESCS'02)
Terano, T; Deguchi, H; Takadama K
English, Editor, Springer-Verlag Tokyo, 2003

Multi-Agent-Based Simulation III, The 4th International Workshop on Multi-Agent Based Simulation (MABS'03), Lecture Notes in Artificial Intelligence, Vol. 2927
Takadama, K; Suematsu, Y. L; Sugimoto, N; Nawa, N. E; Shimohara, K
English, Joint work, Towards Verification and Validation in Multiagent-Based Systems and Simulations: Analyzing Different Learning Bargaining Agents, Springer-Verlag, 2003

Meeting the Challenge of Social Problems via Agent-Based Simulation: Post Proceedings of The Second International Workshop on Agent-based Approaches in Economic and Social Complex Systems (AESCS'02)
Suematsu, Y. L; Takadama, K; Nawa, N. E; Shimohara, K; Katai O
English, Joint work, The X-MAS SYSTEM: Toward Simulation Systems for Cross-Validation in Multiagent-based Simulation, Springer-Verlag Tokyo, 2003

マルチエージェント学習 - 相互作用の謎に迫る -
高玉 圭樹
Japanese, Editor, コロナ社, 2003

Agent-based Approaches in Economic and Social Complex Systems
K. Takadama; K. Shimohara
English, Joint work, The Hare and The Tortoise Cumulative Progress in Agent-Based Simulation, The IOS Press, 2002

Learning Classifier Systems Meet Multiagent Environment
192, 210, Advances in Learning Classifier Systems, 2001, Peer-reviwed

Learning Classifier Systems meet Multiagent Environments
K. Takadama; T. Terano; K. Shimohara i; P.L. Lanzi; W. Stolzmann; S.W. Wilson
English, Advances in Learning Classifier Systems,Lecture Notes in Artificial Intelligence,Springer--Verlag, 2001

Towards a Multiagent Design Principle Analyzing an Organizational-Learning Oriented Classifier System
K. Takadama; T. Terano; K. Shimohara; K. Hori; S.NakasukaV. in Loia; S. Sessa
English, Soft Computing Agents: New Trends for Designing Autonomous Systems, The series of Studies in Fuzziness and Soft Computing, Springer--Verlag, 2001

Toward Cumulative Progress in Agent-Based Simulation
K. Takadama; K. Shimohara; in T. Terano; T. Nishida; A. Namatame; S. Tsumoto; Y. Ohsawa; nd T; Washio (E
English, New Frontiers in Artificial Intelligence -- Joint JSAI 2001 WorkshopPost-Proceedings --, Lecture Notes in Computer Science, Springer--Verlag, 2001

How to design good rules for multiple learning agents in scheduling problems?
K. Takadama; M. Watabe; K. Shimohara; S. Nakasuka; H. Nakashima; C. Zhang
English, Lecture Notes in Artificial Intelligence,Springer-Verlag, 1999

Analyzing the Roles of Problem Solving and Learning in Organizational-Learning Oriented Classifier System
K. Takadama; S. Nakasuka; T. Terano; H.Y. Lee; H; Motoda
English, Lecture Notes in ArtificialIntelligence,Springer-Verlag, 1998

Grammatical Learning Model for Adaptive Collective Behaviors in Multiple Robots
K. Takadama; K. Hajiri; T. Nomura; K. Shimohara; S. Nakasuka; in; G. Paun; A. Salomaa
English, Grammatical Models of Multi-Agent Systems, Gordon and Breach, 1998

Artificial Intelligence for Spacecraft Location Estimation based on Craters
Takadama, K; Uwano, F; Waragai, Y; Nakari, I; Kamata, H; Ishida, T; Fukuda, S; Sawai, S; Sakai, S
Joint work, CRC Press

REM Estimation Based on Accelerometer by Excluding Other Stages and Two-Scale Smoothing
Shintani, D; Nakari, I; Washizaki, S; Takadama, K
The 46th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC2024), (to appear), Florida, Peer-reviewed, United States
Jul. 2024
15 Jul. 2024- 19 Jul. 2024

Generating High-Dimensional Prototypes with a Classifier System by Evolving in Latent Space
Yatsu, N; Shiraishi, H; Sato, H; Takadama, K
Genetic and Evolutionary Computation Conference (GECCO 2024), Companion, (to appear), Invited, Melbourne, Australia
Jul. 2024
14 Jul. 2024- 18 Jul. 2024

Approximating Pareto Local Optimal Solution Networks
Tanaka, S; Ochoa, G; Liefooghe, A; Takadama, K; Sato, H
Genetic and Evolutionary Computation Conference (GECCO 2024), (to appear), Melbourne, Peer-reviewed, Australia
Jul. 2024
14 Jul. 2024- 18 Jul. 2024

Multi-Layer Cortical Learning Algorithm for Forecasting Time-Series Data with Smoothly Changing Variation Patterns
Fujino, K; Takadama, K; Sato, H
2024 IEEE Congress on Evolutionary Computation (CEC2024), (to appear), Yokohama, Peer-reviewed, Japan
2024
30 Jun. 2024- 05 Jul. 2024

Design of Generalized and Specialized Helper Objectives for Multi-objective Continuous Optimization Problems
Mochizuki, K; Ishizuka, T; Yatsu, N; Sato, H; Takadama, K
2024 IEEE Congress on Evolutionary Computation (CEC2024), (to appear), Yokohama, Peer-reviewed, Japan
2024
30 Jun. 2024- 05 Jul. 2024

From multipoint search to multiarea search: Novelty-based multi-objectivization for unbounded search space optimization
Ishizawa; Sato, H; Takadama, K
2024 IEEE Congress on Evolutionary Computation (CEC2024), (to appear), Yokohama, Peer-reviewed, Japan
2024
30 Jun. 2024- 05 Jul. 2024

Designing Helper Objectives in Mult-objectivization
Tanaka, S; Liefooghe, A; Takadama, K; Sato, H
2024 IEEE Congress on Evolutionary Computation (CEC2024), (to appear), Yokohama, Peer-reviewed, Japan
2024
30 Jun. 2024- 05 Jul. 2024

Pareto Front Estimation Model Optimization for Aggregative Solution Set Representation
Okumura, N; Takadama, K; Sato, H
2024 IEEE Congress on Evolutionary Computation (CEC2024), (to appear), Yokohama, Peer-reviewed, Japan
2024
30 Jun. 2024- 05 Jul. 2024

Prototype Generation with sUpervised Classifier system on kNN matching
Yatsu, N; Shiraishi, H; Sato, H; Takadama, K
2024 IEEE Congress on Evolutionary Computation (CEC2024), (to appear), Yokohama, Peer-reviewed, Japan
2024
30 Jun. 2024- 05 Jul. 2024

Push and Pull Search with Directed Mating for Constrained Multi-objective Optimization
Takamiya, R; Miyakawa, M; Takadama, K; Sato, H
2024 IEEE Congress on Evolutionary Computation (CEC2024), (to appear), Yokohama, Peer-reviewed, Japan
2024
30 Jun. 2024- 05 Jul. 2024

Should Multi-objective Evolutionary Algorithms Use Always Best Non-dominated Solutions as Parents?
Sato, K; Okumura, N; Takadama, K; Sato, H
2024 IEEE Congress on Evolutionary Computation (CEC2024), (to appear), Yokohama, Peer-reviewed, Japan
2024
30 Jun. 2024- 05 Jul. 2024

Evolutionary Constrained Multi-Factorial Optimization Based on Task Similarity
Kawakami, S; Takadama, K; Sato, H
2024 IEEE Congress on Evolutionary Computation (CEC2024), (to appear), Yokohama, Peer-reviewed, Japan
2024
30 Jun. 2024- 05 Jul. 2024

NREM3 Sleep Stage Estimation Based on Accelerometer by Body Movement Count and Biological Rhythms
Shintani, D; Nakari, I; Washizaki, S; Takadama, K
The AAAI 2024 Spring Symposia, Impact of GenAI on Social and Individual Well-being, AAAI (The Association for the Advancement of Artificial Intelligence), Palo Alto, Peer-reviewed, United States
27 Mar. 2024

Sleep Stage Estimation by Introduction of Sleep Domain Knowledge to AI: Towards Personalized Sleep Counseling System with GenAI
Nakari, I; Takadama, K
The AAAI 2024 Spring Symposia, Impact of GenAI on Social and Individual Well-being, AAAI (The Association for the Advancement of Artificial Intelligence), Palo Alto, Peer-reviewed, United States
25 Mar. 2024

The Challenges for GenAI for social and individual Well-being
Kido T; Takadama, K
The AAAI 2024 Spring Symposia, Impact of GenAI on Social and Individual Well-being, AAAI (The Association for the Advancement of Artificial Intelligence), Palo Alto, Peer-reviewed, United States
25 Mar. 2024

What is a correct output by Generative AI from the viewpoint of Well-being? Perspective from sleep stage estimation
Takadama, K
The AAAI 2024 Spring Symposia, Impact of GenAI on Social and Individual Well-being, AAAI (The Association for the Advancement of Artificial Intelligence), Palo Alto, Peer-reviewed, United States
25 Mar. 2024

解集合アグリゲーションのためのパレートフロントモデルの最適化, テス問題とビル運用最適化問題における検証
奥村 成; 太田 恵大; 佐藤 冬樹; 高玉 圭樹; 佐藤 寛之
進化計算学会，第25回進化計算学会研究会, 神奈川（東京都市大学横浜キャンパス）, Japan
22 Mar. 2024

パレートフロントのアンサンブル推定に関する検討
木川田 幸翼; 奥村 成; 高玉 圭樹; 佐藤 寛之
進化計算学会，第25回進化計算学会研究会, 神奈川（東京都市大学横浜キャンパス）, Japan
22 Mar. 2024

大脳新皮質学習に基づく多変量の時系列予測の補完に関する検討
丹羽 和磨; 藤野 和志; 高玉 圭樹; 佐藤 寛之
計測自動制御学会，第51回知能システムシンポジウム, 大阪（近畿大学）, Japan
11 Mar. 2024

大脳新皮質学習に基づくサッカード運動の模倣に関する検討
松田 優一; 丹羽 和磨; 高玉 圭樹; 佐藤 寛之
計測自動制御学会，第51回知能システムシンポジウム, 大阪（近畿大学）, Japan
11 Mar. 2024

タスククラスタリングメタ逆強化学習に向けたファインチューンベース特徴量
杭谷 拓海; 高玉 圭樹
計測自動制御学会，第51回知能システムシンポジウム, 大阪（近畿大学）, Japan
11 Mar. 2024

マルチエージェント協調問題における協調行動獲得のための内部報酬設計
竹内 意織; 高玉 圭樹
計測自動制御学会，第51回知能システムシンポジウム, 大阪（近畿大学）, Japan
11 Mar. 2024

洋上の航空路への合流へ向けたロバストスケジューリング
石塚 智貴; 佐藤 寛之; 高玉 圭樹
進化計算学会，第17回進化計算シンポジウム 2023, 神奈川県、小田原, Japan, pp. 462-466
23 Dec. 2023

多目的連続最適化問題における変数間属性を考慮したヘルパー関数の設計
望月 啓吾; 谷津 直弥; 佐藤 寛之; 高玉 圭樹
進化計算学会，第17回進化計算シンポジウム 2023, 神奈川県, Japan, pp. 441-448
23 Dec. 2023

PSOとCMA-ESの相互支援による動的関数のピーク追従
藤田 翔英; 石澤 竜希; 高玉 圭樹; 佐藤 寛之
進化計算学会，第17回進化計算シンポジウム 2023, 神奈川県、小田原, Japan, pp. 372-379
23 Dec. 2023

目的関数変形による局所解探索の促進
空閑 智也; 髙玉 圭樹; 佐藤 寛之
神奈川県、小田原, Japan, pp. 354-358
23 Dec. 2023

タスク間類似度を用いる制約付き進化多因子最適化に関する一検討
川上 紫央; 高玉 圭樹; 佐藤 寛之
進化計算学会，第17回進化計算シンポジウム 2023, 神奈川県、小田原, Japan, pp. 338-345
23 Dec. 2023

k近傍照合を用いた進化的ルール学習によるWeighted Prototype Selection
谷津 直弥; 白石 洋輝; 佐藤 寛之; 高玉 圭樹
進化計算学会，第17回進化計算シンポジウム 2023, 神奈川県、小田原, Japan, pp. 309-316
23 Dec. 2023

非有界探索空間最適化に向けたNovelty based Multi-Objectivizationによる多峰性多点探索
石澤 竜希; 佐藤 寛之; 高玉 圭樹
進化計算学会，第17回進化計算シンポジウム 2023, 神奈川県、小田原, Japan, pp. 250-257
22 Dec. 2023

パレート局所解ネットワークの近似的な構築
田中 彰一郎; Gabriela Ochoa; Arnaud Liefooghe; 高玉 圭樹; 佐藤 寛之
進化計算学会，第17回進化計算シンポジウム 2023, 神奈川県、小田原, Japan, pp. 224-233
22 Dec. 2023

多目的進化計算における非劣解アーカイブからの親集合の再選択に関する検討
佐藤 和磨; 奥村 成; 高玉 圭樹; 佐藤 寛之
進化計算学会，第17回進化計算シンポジウム 2023, 神奈川県、小田原, Japan, pp. 151-157
22 Dec. 2023

制約付き多目的最適化のためのプッシュ・プル探索における指向性交配
高宮 諒翔; 宮川 みなみ; 高玉 圭樹; 佐藤 寛之
進化計算学会，第17回進化計算シンポジウム, 神奈川県、小田原, Japan, pp. 19-26
22 Dec. 2023

A Preliminary Study on Ensemble Pareto Front Estimation
Kikawada, K; Takagi, T; Okumura, N; Takadama, K; Sato, H
The 5th ASEAN UEC Workshop 2023, Chofu, Tokyo, Japan
10 Dec. 2023
Dec. 2023

A Preliminary Study on Spatially Distributed Data Forecast Using Cortical Learning Algorithm
Niwa, K; Aoki, T; Fujino, K; Takadama, K; Sato, H
The 5th ASEAN UEC Workshop 2023, Chofu, Tokyo, Japan, Domestic conference
10 Dec. 2023
Dec. 2023

A Preliminary Study on Solution Generation for Evolutionary Multi-factorial Optimization
Kawakami, S; Takadama, K; Sato, H
The 5th ASEAN UEC Workshop 2023, Chohu, Tokyo, Japan
10 Dec. 2023
Dec. 2023

マルチエージェント強化学習におけるカリキュラム学習適用に向けた観測選択アルゴリズムの設計
坂上 凜矩; 高玉 圭樹
計測自動制御学会，システム・情報部門学術講演会 2023 (SSI2023), 計測自動制御学会, 江東区(芝浦工業大学)、東京都, Japan
11 Nov. 2023
10 Nov. 2023- 12 Nov. 2023

非通信マルチエージェント強化学習による即時的環境変化の追従性に関する一考察
上野 史; 高玉 圭樹
計測自動制御学会，システム・情報部門学術講演会 2023 (SSI2023), 計測自動制御学会, 江東区(芝浦工業大学)・東京都, Japan, pp. 326-331
11 Nov. 2023
10 Nov. 2023- 12 Nov. 2023

衝突危険領域と他船の衝突回避方針推定を考慮したマルチエージェント深層強化学習
戸板 佳祐; 高玉 圭樹
計測自動制御学会，システム・情報部門学術講演会 2023 (SSI2023), 計測自動制御学会, 江東区(芝浦工業大学)、東京都
Nov. 2023
10 Nov. 2023- 12 Nov. 2023

準最適なデモンストレーションに対応するアーカイブに基づくマルチエージェント敵対的強化学習
植木駿介; 高玉 圭樹
計測自動制御学会，システム・情報部門学術講演会 2023 (SSI2023), 計測自動制御学会, 江東区(芝浦工業大学)、東京都
Nov. 2023
10 Nov. 2023- 12 Nov. 2023

Alzheimer Dementia Detection Based on Time-series Instability of Heart Rate
Matsuda, N; Nakari, I; Takadama, K; Katayama, K; Shiraishi, M; Ohira, Y
Oral presentation, The 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC2023), Sydney, Peer-reviewed, Australia, International conference
25 Jul. 2023
24 Jul. 2023- 27 Jul. 2023

Personalized Non-contact Sleep Stage Estimation with Weighted Probability Estimation by Ultradian Rhythm
Nakari, I; Matsuda, N; Takadama, K
The 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC2023), Sydney, Peer-reviewed, Australia, International conference
25 Jul. 2023
24 Jul. 2023- 27 Jul. 2023

Exploring High-dimensional Rules Indirectly via Latent Space Through a Dimensionality Reduction for XCS
Yatsu, N; Shiraishi, H; Sato, H; Takadama, K
Genetic and Evolutionary Computation Conference (GECCO 2023), Lisbon, Peer-reviewed, Portugal
17 Jul. 2023
15 Jul. 2023- 19 Jul. 2023

Toward Unbounded Search Space Exploration by Particle Swarm Optimization in Multi-Modal Optimization Problem
Ishizawa, R; Kuga, T; Maekawa Y; Sato, H; Takadama, K
2023 IEEE Congress on Evolutionary Computation (CEC2023), Peer-reviewed
01 Jul. 2023- 05 Jul. 2023

運転事故防止に向けた睡眠トータルケア：運転前の睡眠状況把握と運転後の睡眠改善
高玉 圭樹
Public discourse, 自動車技術会, Invited, 自動車技術会, 千代田区、東京, Japan
16 Jun. 2023

戸板 佳祐, 中理 怡恒, 高玉 圭樹
衝突回避方針の相互選択状況下における方針推定に基づくマルチエージェント強化
計測自動制御学会，第50回知能システムシンポジウム
29 Mar. 2023

マルチエージェント敵対環境における協調行動のためのカリキュラム学習の設計
坂上 凜矩; 戸板 佳祐; 高玉 圭樹
計測自動制御学会，第50回知能システムシンポジウム
29 Mar. 2023

How to handle well-being in Socially Responsible AI? - Findings from sleep perspective
Takadama, K
Oral presentation, The AAAI 2023 Spring Symposia, The AAAI 2023 Spring Symposia, Socially Responsible AI for Well-being, AAAI, AAAI (The Association for the Advancement of Artificial Intelligence), San Fransico, Peer-reviewed, United States, International conference
29 Mar. 2023

Evaluation of sleep quality and thermal environment according to nap time
Nakai, M; Ashikaga, T; Shimizu, J; Takadama, K
Oral presentation, The AAAI 2023 Spring Symposia, The AAAI 2023 Spring Symposia, Socially Responsible AI for Well-being, AAAI, AAAI (The Association for the Advancement of Artificial Intelligence), San Fransico, Peer-reviewed, United States, International conference
28 Mar. 2023

Sleep Stage Estimation based on The Estimated Probability of each Sleep Stage by Learning with Specialized Models
Nakari, I; Takadama, K
Oral presentation, The AAAI 2023 Spring Symposia, The AAAI 2023 Spring Symposia, Socially Responsible AI for Well-being, AAAI, AAAI (The Association for the Advancement of Artificial Intelligence), San Fransico, Peer-reviewed, International conference
28 Mar. 2023

The Challenges for Socially Responsible AI for Well-being
Kido, T; Takadama, K
The AAAI 2023 Spring Symposia, AAAI (The Association for the Advancement of Artificial Intelligence), AAAI (The Association for the Advancement of Artificial Intelligence), San Fransico, Peer-reviewed, United States, International conference
27 Mar. 2023

Adaptive Synapse Adjustment and Decoding in Action-prediction Cortical Learning Algorithm
Fujino, K; Aoki, T; Takadama, K; Sato, H
The 14th World Congress on Nature and Biologically Inspired Computing (NaBIC2022), Peer-reviewed
02 Mar. 2023

Preliminary Study of Adaptive Synapse Generation in Cortical Learning Algorithm
K. Aoki; Takadama, K; Sato, H
2023 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2023), Peer-reviewed
02 Mar. 2023

Similarity-based Multi-factorial Evolutionary Algorithm for Binary Optimization Problems
Kawakami, S; Tanaka, S; Takadama, K; Sato, H
2023 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2023), Peer-reviewed
02 Mar. 2023

Multi-layer Cortical Learning Algorithm for Trend Changing Time-series Forecast
Fujino, K; Aoki, T; Takadama, K; Sato, H
Oral presentation, 2023 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2023), Peer-reviewed
01 Mar. 2023

少数個体に向けた局所解アーカイブに基づく局所解探索のための複数粒子群最適化
前川 裕介; 佐藤 寛之; 高玉 圭樹
進化計算学会，第16回進化計算シンポジウム 2022
17 Dec. 2022

多因子CMNKランドスケープ問題において類似目的関数を利用する進化計算
川上 紫央; 田中 彰一郎; 高玉 圭樹; 佐藤 寛之
進化計算学会，第16回進化計算シンポジウム 2022
17 Dec. 2022

車列表現の一般化による多様な車列に適用可能な車両入替手順の進化的最適化
古屋 敬祐; 中理 怡恒; 長濱 章仁; 佐藤 寛之; 高玉 圭樹
進化計算学会，第16回進化計算シンポジウム 2022
17 Dec. 2022

進化的ルール学習と次元削減・生成モデルのハイブリッドモデルにおける観測空間での報酬による潜在空間を経由したルール学習手法
谷津 直弥; 白石 洋輝; 佐藤 寛之; 髙玉 圭樹
進化計算学会，第16回進化計算シンポジウム 2022
17 Dec. 2022

学習分類子システムによる深層学習モデルの複数判断ルールの獲得
新谷 大樹; 谷津 直弥; 白石 洋輝; 高玉 圭樹
進化計算学会，第16回進化計算シンポジウム 2022
17 Dec. 2022

無限探索空間のマルチモーダル最適化問題に向けた粒子群最適化による動的探索範囲拡張
石澤 竜希; 空閑 智也; 前川 祐介; 佐藤 寛之; 高玉圭樹
Oral presentation, 進化計算学会，第16回進化計算シンポジウム 2022
17 Dec. 2022

マルチファネル構造を持つ単一目的最適化問題の多目的化による緩和
田中 彰一郎; 高玉 圭樹; 佐藤 寛之
Oral presentation, 進化計算学会，第16回進化計算シンポジウム 2022
17 Dec. 2022

A Study on Synergy of Adaptive Synapse Arrangement and Column-based Decoder in Cortical Learning Algorithm
Aoki, T; Takadama, K; Sato, H
English, Joint 12th International Conference on Soft Computing and Intelligent Systems and 21th International Symposium on Advanced Intelligent Systems (SCIS & ISIS2022), IEEE, http://xplorestaging.ieee.org/ielx7/10001867/10001868/10001918.pdf?arnumber=10001918
29 Nov. 2022
29 Nov. 2022- 29 Nov. 2022

Adaptive Synapse Adjustment for Multivariate Cortical Learning Algorithm.
Fujino, K; Aoki, T; Takadama, K; Sato, H
English, Joint 12th International Conference on Soft Computing and Intelligent Systems and 21th International Symposium on Advanced Intelligent Systems (SCIS & ISIS2022), IEEE, http://xplorestaging.ieee.org/ielx7/10001867/10001868/10002035.pdf?arnumber=10002035
29 Nov. 2022
29 Nov. 2022- 29 Nov. 2022

Niching Migratory Multi-Swarm Optimiser by Replacing Generation and Deletion with Movement for Real Robots: adjusting swarm size based on convergence
Maekawa, Y; Sato, H; Takadama, K
The 16th International Symposium on Distributed Autonomous Robotic Systems (DARS 2022), Peer-reviewed
29 Nov. 2022

反復的に推定モデルを更新する教師あり多目的最適化アルゴリズムに関する検討
高木 智章; 高玉 圭樹; 佐藤 寛之
計測自動制御学会，システム・情報部門 学術講演会 2022 (SSI2022)
27 Nov. 2022

階層型メタ強化学習におけるサブポリシーの獲得と状態マッピングによる未学習領域への適応
加藤 駿; 中理 怡恒; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2022 (SSI2021), 東大阪市(近畿大学)・大阪, Domestic conference
25 Nov. 2022

二個体協調における自由度に基づくマルチエージェント逆強化学習
植木駿介; 亀谷 長太; 戸板 佳祐; 中理 怡恒; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2022 (SSI2021), 東大阪市(近畿大学)・大阪, Domestic conference
25 Nov. 2022

大脳新皮質学習アルゴリズムにおけるシナプスの適応配置とカラムに基づくデコーダの協調
青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2022 (SSI2021), 東大阪市(近畿大学)・大阪
25 Nov. 2022

大脳新皮質学習における抑制性セルの導入と効果
後藤 祐希; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2022 (SSI2021), 東大阪市(近畿大学)・大阪, Domestic conference
25 Nov. 2022

大脳新皮質学習における適応的なシナプス調整と予測値デコードの効果
藤野 和志; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2022 (SSI2021), 東大阪市(近畿大学)・大阪, Domestic conference
25 Nov. 2022

多因子離散最適化問題において目的関数の類似度を計測して利用する進化計算の効果
川上 紫央; 田中 彰一郎; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2022 (SSI2021), 東大阪市(近畿大学)・大阪
20 Nov. 2022

複数の時間窓の生体振動データを学習した機械学習の組み合わせによる無拘束型睡眠段階推定
中理 怡恒; 高玉 圭樹
第3回 ヘルスケア・医療情報通信技術研究会 (MICT)，電子情報通信学会
18 Nov. 2022

Prototype models for predicting vehicle types generated in heterogeneous traffic simulation
Nagahama, A; Wada, T; Takadama, K; Yanagisawa, D; Nishinari, K; Tanaka, K
Traffic and Granular Flow 2022, Peer-reviewed
16 Oct. 2022

Bus Transportation Network Optimization in Competition of Two Bus Companies Starting with Similar/Different Routes.
Zhou, R; Nakari, I; Yatsu, N; Takadama, K
English, SICE Annual Conference 2022
09 Sep. 2022
09 Sep. 2022- 09 Sep. 2022

Supervised Multi-objective Optimization Algorithm Using Estimation
Takagi, M; Takadama, K; Sato, H
English, 2022 IEEE Congress on Evolutionary Computation (CEC2022)
20 Jul. 2022
20 Jul. 2022- 20 Jul. 2022

Impacts of Single-objective Landscapes on Multi-objective Optimization
Tanaka, S; Takadama, K; Sato, H
English, 2022 IEEE Congress on Evolutionary Computation (CEC2022)
20 Jul. 2022
20 Jul. 2022- 20 Jul. 2022

XCSR with VAE using Gaussian Distribution Matching: From Point to Area Matching in Latent Space for Less-overlapped Rule Generation in Observation Space,
Yatsu, N; Shiraishi, H; Sato, H; Takadama, K
English, 2022 IEEE Congress on Evolutionary Computation (CEC2022), IEEE, http://xplorestaging.ieee.org/ielx7/9870216/9870201/09870349.pdf?arnumber=9870349
20 Jul. 2022
20 Jul. 2022- 20 Jul. 2022

Beta Distribution-based XCS Classifier System
Shiraishi, H; Hayamizu, Y; Sato, H; Takadama, K
English, 2022 IEEE Congress on Evolutionary Computation (CEC2022)
20 Jul. 2022
20 Jul. 2022- 20 Jul. 2022

Unstable Circadian Rhythm of Heart Rate of Alzheimer Dementia Based on Biological Data of Mattress Sensor.
Matsuda, N; Nakari, I; Takadama, K
English, The 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC2022)
12 Jul. 2022
12 Jul. 2022- 12 Jul. 2022

Non-Contact REM Sleep Estimation by Time-Series Confidence of Predictions: From Binary to Continuous Prediction in Machine Learning for Biological Data
Nakari, I; Matsuda, N; Takadama, K
English, The 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC2022)
12 Jul. 2022
12 Jul. 2022- 12 Jul. 2022

Can the Same Rule Representation Change its Matching Area? Enhancing Representation in XCS for Continuous Space by Probability Distribution in Multiple Dimension
Shiraishi, H; Hayamizu, Y; Sato, H; Takadama, K
English, Genetic and Evolutionary Computation Conference (GECCO 2022), ACM, https://dl.acm.org/doi/pdf/10.1145/3512290.3528874
12 Jul. 2022
12 Jul. 2022- 12 Jul. 2022

Absumption based on Overgenerality and Condition-Clustering based Specialization for XCS with Continuous-Valued Inputs
Shiraishi, H; Hayamizu, Y; Sato, H; Takadama, K
English, Genetic and Evolutionary Computation Conference (GECCO 2022), ACM, https://dl.acm.org/doi/pdf/10.1145/3512290.3528841
11 Jul. 2022
11 Jul. 2022- 11 Jul. 2022

マットレスセンサから得られた生体振動データの機械学習による特徴抽出に基づく健常者と睡眠時無呼吸症候群の違いの発見
中理 怡恒; 高玉 圭樹
Oral presentation, Japanese, 第47回日本睡眠学会シンポジウム，日本睡眠学会, 京都市、京都府, Domestic conference
30 Jun. 2022

Random Forestsによる睡眠時無呼吸症候群と健常者の学習結果の比較に基づく特徴抽出と解釈可能な判定
中理 怡恒; 高玉 圭樹
Oral presentation, Japanese, 第36回人工知能学会全国大会, Domestic conference
15 Jun. 2022

A thermal environment that promotes efficient napping
Ohga, T; Ashikaga T; Nakai M; Takadama, K
English, The AAAI 2022 Spring Symposia, How Fair is Fair? Achieving Wellbeing AI
22 Mar. 2022
22 Mar. 2022- 22 Mar. 2022

Analysis of Circadian Rhythm Estimation Process for Improving the Accuracy of Alzheimer Dementia Detection
Matsuda, N; Senju, T; Nakari, I; Takadama, K
English, The AAAI 2022 Spring Symposia, How Fair is Fair? Achieving Wellbeing AI
22 Mar. 2022
22 Mar. 2022- 22 Mar. 2022

REM Estimation Based on Combination of Multi-Timescale Estimations and Automatic Adjustment of Personal Bio-vibration Data of Mattress Sensor
Nakari, I; Matsuda, N; Takadama, K
English, The AAAI 2022 Spring Symposia, How Fair is Fair? Achieving Wellbeing AI
22 Mar. 2022
22 Mar. 2022- 22 Mar. 2022

How to cope with bias in Well-being AI - Towards fairness in Well-being AI by personal and long-term evaluation?
Takadama, K
English, The AAAI 2022 Spring Symposia, How Fair is Fair? Achieving Wellbeing AI
21 Mar. 2022
21 Mar. 2022- 21 Mar. 2022

The Challenges for Fairness and Well-being - How Fair is Fair? Achieving Well-being AI
Kido, T; Takadama, K
The AAAI 2022 Spring Symposia, How Fair is Fair? Achieving Wellbeing AI, AAAI (The Association for the Advancement of Artificial Intelligence), Peer-reviewed
21 Mar. 2022

渋滞緩和に向けた交通流率と車列の不安定性を考慮した進化計算よる車両順最適化
古屋 敬祐; 中理 怡恒; 河野 航大; 長濱 章仁; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 第49回知能システムシンポジウム, 計測自動制御学会(オンライン開催), Domestic conference
15 Mar. 2022

他船のモデル化を通した目的地と衝突回避方針の同時推定に基づくマルチエージェント強化学習
戸板 佳祐; 前川 裕介; 加藤 駿; 福本 有季子; 中理 怡恒; 高玉 圭樹
Oral presentation, Japanese, 第49回知能システムシンポジウム, 計測自動制御学会(オンライン開催), Domestic conference
15 Mar. 2022

交互的路線の奪い合いによる複数バス会社のバス路線網の分析
周 仁亮; 谷津 直弥; 中理 怡恒; 高玉 圭樹
Oral presentation, Japanese, 第49回知能システムシンポジウム, 計測自動制御学会(オンライン開催), Domestic conference
15 Mar. 2022

覚醒とNon-REM睡眠の影響を除去した体動の出現頻度に基づく非拘束型REM睡眠推定
嘉村 魁人; 松田 尚也; 千住 太希; 中理 怡恒; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 第49回知能システムシンポジウム, 計測自動制御学会(オンライン開催), Domestic conference
14 Mar. 2022

適応範囲の拡大に向けたMAMLとMLSHの組み合わせによるメタ強化学習
加藤 駿; 速水 陽平; 中理 怡恒; 高玉 圭樹
Oral presentation, Japanese, 第49回知能システムシンポジウム, 計測自動制御学会(オンライン開催), Domestic conference
14 Mar. 2022

多目的化が最適化に与える影響に関する基礎的検討
田中 彰一郎; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2022 (SSI2021), 東大阪市(近畿大学)・大阪, Domestic conference
25 Jan. 2022

VAEを用いた学習分類子システムによる高次元マルチステップ問題の汎用的ルール学習
谷津直弥; 白石洋輝; 佐藤寛之; 高玉圭樹
Oral presentation, Japanese, 進化計算学会，第15回進化計算シンポジウム2021(オンライン開催), Domestic conference
26 Dec. 2021

β分布に基づく学習分類子システム
白石 洋輝; 速水 陽平; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第15回進化計算シンポジウム2021(オンライン開催), Domestic conference
26 Dec. 2021

単一の目的関数のランドスケープが多目的最適化に与える影響
田中彰一郎; 高玉圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第15回進化計算シンポジウム2021(オンライン開催)
26 Dec. 2021

進化型多目的最適化における重みベクトル選択に基づく解集団分割と領域別探索
河野航大; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算学会，第15回進化計算シンポジウム2021(オンライン開催), International conference
26 Dec. 2021

3目的最適化結果の可視化法に関する比較検討
高木智章; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算学会，第15回進化計算シンポジウム2021(オンライン開催), Domestic conference
26 Dec. 2021

複数解探索のための収束状況に応じた複数群間移動に基づく実ロボット適用に向けた粒子群最適化
前川裕介; 河野航大; 佐藤寛之; 高玉圭樹
Oral presentation, Japanese, 進化計算学会，第15回進化計算シンポジウム 2021(オンライン開催), Domestic conference
25 Dec. 2021

他エージェントの不確実性にロバストな経路獲得に向けたマルチエージェント逆強化学習
福本 有季子; 速水 陽平; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2021 (SSI2021) (オンライン開催), Domestic conference
22 Nov. 2021

実数値学習分類子システムにおける最近傍ルールの汎化レベルを継承する被覆法
白石 洋輝; 速水 陽平; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2021 (SSI2021) (オンライン開催)
22 Nov. 2021

不確実な生体信号データの再学習を有する二段Evidential Neural Networkによる睡眠段階推定
千住 太希; 中理 怡恒; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2021 (SSI2021) (オンライン開催)
22 Nov. 2021

他エージェントの不確実性にロバストな経路獲得に向けたマルチエージェント逆強化学習
福本 有季子; 速水 陽平; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2021 (SSI2021) (オンライン開催)
20 Nov. 2021

実数値学習分類子システムにおける最近傍ルールの汎化レベルを継承する被覆法
白石 洋輝; 速水 陽平; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2021 (SSI2021) (オンライン開催)
20 Nov. 2021

不確実な生体信号データの再学習を有する二段Evidential Neural Networkによる睡眠段階推定
千住 太希; 中理 怡恒; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2021 (SSI2021) (オンライン開催)
20 Nov. 2021

オクルージョン下における関節点の信頼値と塗りつぶしに基づく複数人の再帰的個別姿勢推定
荒井 亮太郎; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2021 (SSI2021) (オンライン開催)
20 Nov. 2021

大脳新皮質学習の階層化における抑制フィードバックの検討
青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2021 (SSI2021) (オンライン開催)
20 Nov. 2021

変分ベイズに基づく高精度画像照合による無人月面探査機の画像航行に関する研究
丹治 寛樹; 大関 裕太; 伊藤 充輝; 神田 達也; 鎌田 弘之; 高玉 圭樹; 石田 貴行; 福田 盛介; 澤井 秀次郎; 坂井 真一郎
Oral presentation, Japanese, 第65回宇宙科学技術連合講演会，日本航空宇宙学会(オンライン開催)
11 Nov. 2021

睡眠時の短時間覚醒の特徴分析と睡眠時無呼吸症候群の判定の可能性
中理 怡恒; 高玉 圭樹
Oral presentation, Japanese, 第3回 ヘルスケア・医療情報通信技術研究会 (MICT)，電子情報通信学会，信学技報(オンライン開催)
05 Nov. 2021

三角関数近似による心拍数の概日リズムにおける正弦波成分の不安定さに基づくアルツハイマー型認知症判定
松田 尚也; 中理 怡恒; 高玉 圭樹
Oral presentation, Japanese, 第3回 ヘルスケア・医療情報通信技術研究会 (MICT)，電子情報通信学会，信学技報(オンライン開催)
05 Nov. 2021

Alzheimer Dementia Detection based on Instability of Circadian Rhythm Waves of Heartrate
Matsuda, N; Nakari, I; Takadama, K
English, The 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC2021)
31 Oct. 2021
31 Oct. 2021- 31 Oct. 2021

Sleep Apnea Syndrome Detection Based on Degree of Convexity of Logaritshmic Spectrum Calculated from Overnight Biovibration Data of Mattress Sensor.
Nakari, I; Takadama, K
English, The 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC2021)
31 Oct. 2021
31 Oct. 2021- 31 Oct. 2021

無拘束型マットセンサによる睡眠時無呼吸症候群のスクリーニングの可能性
高玉 圭樹
Oral presentation, Japanese, 第46回日本睡眠学会シンポジウム, 日本睡眠学会, 福岡県・福岡/オンライン
24 Sep. 2021

グリッド型多目的進化計算における可変エリート集団に関する検討
加納 謙介; 高木 智章; 田中 彰一郎; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会， 電気学会，第30回インテリジェント・システム・シンポジウム (FAN 2021)(オンライン開催)
22 Sep. 2021

ルールの過剰汎化率を考慮したAbsumption に基づく実数値学習分類子システム
白石 洋輝; 速水 陽平; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会， 電気学会，第30回インテリジェント・システム・シンポジウム (FAN 2021)(オンライン開催)
21 Sep. 2021

多目的意思決定を支援する有向パレートフロントの推定
高木 智章; 田中 彰一郎; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会， 電気学会，第30回インテリジェント・システム・シンポジウム (FAN 2021)(オンライン開催)
21 Sep. 2021

吸収マルコフ連鎖に基づく局所解ネットワークに関する検討
田中 彰一郎; 古谷 博史; 日和 悟; 廣安 知之; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第20回進化計算学会研究会(オンライン開催)
08 Sep. 2021

推定を利用する教師あり多目的最適化アルゴリズムに関する検討
高木 智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第20回進化計算学会研究会 (オンライン開催)
08 Sep. 2021

目的関数の類似度を利用した多因子進化計算に関する検討
川上紫央; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第20回進化計算学会研究会(オンライン開催)
08 Sep. 2021

Multi-factorial Evolutionary Algorithm Using Objective Similarity Based Parent Selection
Kawakami, S; Takadama, K; Sato, H
English, 13th EAI International Conference on Bio-inspired Information and Communications Technologies (BICT 2021), Peer-reviewed
01 Sep. 2021
01 Sep. 2021- 01 Sep. 2021

Double-layered Cortical Learning Algorithm for Time-series Data Prediction
Aoki, T; Takadama, K; Sato, H
English, 13th EAI International Conference on Bio-inspired Information and Communications Technologies (BICT 2021)
01 Sep. 2021
01 Sep. 2021- 01 Sep. 2021

Guiding Robot Exploration in Reinforcement Learning via Automated Planning
Hayamizu, Y; Amiri, S; Chandan, K; Takadama, K; Zhang, S
English, The 31st International Conference on Automated Planning and Scheduling (ICAPS 2021), Peer-reviewed
02 Aug. 2021
02 Aug. 2021- 02 Aug. 2021

Misclassification Detection based on Conditional VAE for Rule Evolution in Learning Classifier System
Shiraishi, H; Tadokoro, M; Hayamizu, Y; Fukumoto, Y; Sato; H; Takadama, K
English, Genetic and Evolutionary Computation Conference (GECCO 2021)
12 Jul. 2021
12 Jul. 2021- 12 Jul. 2021

Weight Vector Arrangement Using Virtual Objective Vectors in Decomposition-based MOEA
Takagi, M; Takadama, K; Sato, H
English, 2021 IEEE Congress on Evolutionary Computation (CEC2021), ６, Peer-reviewed
30 Jun. 2021
30 Jun. 2021- 30 Jun. 2021

XCS with Weight-based Matching in VAE Latent Space and Additional Learning of High-Dimensional Data
Tadokoro, M; Sato, H; Takadama, K
English, 2021 IEEE Congress on Evolutionary Computation (CEC2021), Peer-reviewed
29 Jun. 2021
29 Jun. 2021- 29 Jun. 2021

Increasing Accuracy and Interpretability of High-Dimensional Rules for Learning Classifier System
Shiraishi, H; Tadokoro, M; Hayamizu, Y; Fukumoto, Y; Sato; H; Takadama, K
English, 2021 IEEE Congress on Evolutionary Computation (CEC2021), Peer-reviewed
29 Jun. 2021
29 Jun. 2021- 29 Jun. 2021

Alzheimer Dementia Detection based on Circadian Rhythm Disorder of Heartrate
Matsuda, N; Nakari, I; Arai, R; Sato, H; Takadama, K; Hirose, M; Hasegawa, H; Shiraishi, M; Matsuda, T
English, 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021), Peer-reviewed
11 Mar. 2021
11 Mar. 2021- 11 Mar. 2021

正しい意見共有に向けたユーザの投稿頻度を考慮したエージェントネットワークシステム：人とエージェントの関係から人とエージェント集団の関係への展開
山根 大輝; 前川 佳幹; 荒井 亮太郎; 福本 有季子; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 人工知能学会，HAIシンポジウム2021(オンライン開催)
10 Mar. 2021

心拍数から推定した概日/非概日リズムの振幅比率に基づくアルツハイマー型認知症判定
松田 尚也; 荒井 亮太 郎; 藁谷 由香; 中理 怡恒; 佐藤 寛之; 高玉 圭樹; 廣瀬 雅宣; 長谷川 洋; 白石 眞; 松田 隆秀
Oral presentation, Japanese, 計測自動制御学会，第48回知能システムシンポジウム(オンライン開催)
09 Mar. 2021

睡眠段階ごとの生体振動特徴に着目したニューラルネットワークによる推定
千住 太希; 中理 怡恒; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第48回知能システムシンポジウム(オンライン開催)
09 Mar. 2021

大脳新皮質学習における多層化に関する検討
青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，第48回知能システムシンポジウム (オンライン開催)
09 Mar. 2021

説明可能なAI：学習分類子システム
髙玉圭樹
海上技術安全研究所、海洋リスク評価系、知識・データシステム系講演会, Invited
26 Feb. 2021

Finding Many Good Solutions by Multi-Swarm Optimization for Multiple Robots: The Niching Migratory Multi-Swarm Optimiser with Limited Movement
Maekawa, Y; Kawano, K; Kajihara, S; Fukumoto, Y; Sato, H; Takadama, K
English, The 26th International Symposium on Artificial Life and Robotics (AROB 2021)
22 Jan. 2021
22 Jan. 2021- 22 Jan. 2021

重みベクトルと混雑距離における解選択の相互補完に基づく進化計算: MOEA/DとNSGA-IIの融合
河野 航大; 梶原 奨; 田所 優和; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第14回進化計算シンポジウム 2020(オンライン開催)
20 Dec. 2020

学習分類子システムのルール進化に対するConditional VAE に基づく誤判定訂正
白石 洋輝; 田所 優和; 速水 陽平; 福本 有季子; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第14回進化計算シンポジウム 2020(オンライン開催)
20 Dec. 2020

最適化問題の類似性を利用した未知の最適化問題に適した進化計算法の推薦
山本 康平; 高木 智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第14回進化計算シンポジウム 2020(オンライン開催)
20 Dec. 2020

目的関数空間の単位超平面を基準とするパレートフロント推定とその利用
高木 智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第14回進化計算シンポジウム 2020(オンライン開催)
19 Dec. 2020

複数局所解探索のための複数群間移動に基づく粒子群最適化：実ロボット環境に向けた個体数固定と同時移動への展開
前川裕介; 河野航大; 梶原奨; 福本有季子; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第14回進化計算シンポジウム 2020(オンライン開催)
19 Dec. 2020

Efficient Exploration in Reinforcement Learning Leveraging Automated Planning
Hayamizu, Y; Amiri, S; Chandan, K; Takadama, K; Zhang, S
English, The 3rd Robot Learning Workshop: Grounding Machine Learning Development in the Real World
11 Dec. 2020
11 Dec. 2020- 11 Dec. 2020

Column-Based Predictive Value Decoding in Cortical Learning Algorithms.
Aoki, T; Takadama, K; Sato, H
English, Joint 11th International Conference on Soft Computing and Intelligent Systems and 21th International Symposium on Advanced Intelligent Systems (SCIS & ISIS2020)
06 Dec. 2020
06 Dec. 2020- 06 Dec. 2020

Cooperative Multi-agent Inverse Reinforcement Learning Based on Selfish Expert and its Behavior Archives
Fukumoto, Y; Tadokoro, M; Takadama, K
English, The 2020 IEEE Symposium Series on Computational Intelligence (SSCI 2020)
04 Dec. 2020
04 Dec. 2020- 04 Dec. 2020

個別探索から生成された行動系列の優先付けに基づくマルチエジェント逆強化学習
福本 有季子; 速水 陽平; 前川 佳幹; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2020 (SSI2020) (オンライン開催)
16 Nov. 2020

知識の誤りに対する自動計画を利用したモデルベース強化学習のロバスト性
速水 陽平; Zhang Shiqi; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2020 (SSI2020) (オンライン開催)
16 Nov. 2020

VAEの潜在変数空間における分布ベース照合に基づく学習分類子システム
田所 優和; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2020 (SSI2020) (オンライン開催)
16 Nov. 2020

MarioGAN と進化計算による多様なステージ生成に関する検討
熊谷 涼; 高木 智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2020 (SSI2020) (オンライン開催), 計測自動制御学会
15 Nov. 2020

多変量大脳新皮質学習によるデータの連続欠損に対する予測持続に関する検討
長島 晶彦; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2020 (SSI2020) (オンライン開催)
15 Nov. 2020

大脳新皮質学習におけるカラムに基づく予測表現デコーダに関する検討
青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2020 (SSI2020) (オンライン開催)
15 Nov. 2020

Random Forestsによる健常者とSAS患者の学習結果の違いに基づく生体振動データの周波数解析
中理 怡恒; 高玉 圭樹
Oral presentation, Japanese, 第2回 ヘルスケア・医療情報通信技術研究会 (MICT)，電子情報通信学会(オンライン開催), 電子情報通信学会
04 Nov. 2020

密集クレータによる推定誤差を考慮した宇宙探査機の自己位置推定
藁谷 由香; 中理 怡恒; 高玉 圭樹; 鎌田 弘之; 石田 貴行; 福田盛介; 澤井 秀次郎; 坂井 真一郎
Oral presentation, Japanese, 第64回宇宙科学技術連合講演会（オンライン開催）, 日本航空宇宙学会, Domestic conference
30 Oct. 2020

外乱を伴う月面撮影画像と地図画像との高精度照合に関する研究
大関 裕太; 小原 静華; 小松原 燿介; 鎌田 弘之; 高玉 圭樹; 石田 貴行; 福田 盛介; 澤井 秀次郎; 坂井 真一郎
Oral presentation, Japanese, 第64回宇宙科学技術連合講演会（オンライン開催）, 日本航空宇宙学会, Domestic conference
30 Oct. 2020

Towards Accurate Spacecraft Self-Location Estimation by Eliminating Close Craters in Camera-Shot Image
Waragai, Y; Nakari, I; Hayamizu, Y; Takadama, K; Kamata, H; Ishida, T; Fukuda S; Sawai, S; Sakai, S
English, The 15th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS2020)
21 Oct. 2020
21 Oct. 2020- 21 Oct. 2020

Towards Agent Design for Forming a Consensus Remotely Through an Analysis of Declaration of Intent in Barnga Game
Maekawa, Y; Yamaguchi, T; Takadama, K
English, 4th International Conference on Intelligent Human Systems Integration (IHSI 2021)
19 Oct. 2020
19 Oct. 2020- 19 Oct. 2020

仮想目的ベクトル群によるパレートフロントの形状推定
高木 智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第18回進化計算学会研究会（オンライン開催）, 進化計算学会研究会, Domestic conference
16 Sep. 2020

モデルベース強化学習における自動計画を用いた探索戦略
速水 陽平; Amiri Saeid,Chandan Kishan; Zhang Shiqi; 高玉 圭樹
Oral presentation, Japanese, 情報処理学会，第19回情報科学技術フォーラム(Forum on Information Technology: FIT2020)（オンライン開催）, 情報処理学会, 札幌市，北海道, Domestic conference
01 Sep. 2020

Local Covering: Adaptive Rule Generation Method Using Existing Rules for XCS
Tadokoro, M; Hasegawa, S; Tatsumi, T; Sato, H; Takadama, K
English, 2020 IEEE Congress on Evolutionary Computation (CEC2020), 2020 IEEE Congress on Evolutionary Computation (CEC2020)
19 Jul. 2020
19 Jul. 2020- 19 Jul. 2020

Preliminary Study of Adaptive Grid-based Decomposition on Many-objective Evolutionary Optimization
Kanou, K; Takagi, M; Takadama, K; Sato, H
English, International Workshop on Evolutionary Many-objective Optimization (E-MaOP 2020), Genetic and Evolutionary Computation Conference (GECCO 2020)
09 Jul. 2020
09 Jul. 2020- 09 Jul. 2020

Visual Mapping of Multi-objective Optimization Problems and Evolutionary Algorithms
Yamamoto, K; Takagi, M; Takadama, K; Sato, H
English, International Workshop on Visualisation Methods in Genetic and Evolutionary Computation (VizGEC 2020), Genetic and Evolutionary Computation Conference (GECCO 2020)
08 Jul. 2020
08 Jul. 2020- 08 Jul. 2020

Incremental Lattice Design of Weight Vector Set
Takagi, M; Takadama, K; Sato, H
English, International Workshop on Decomposition Techniques in Evolutionary Optimization (DTEO 2020), Genetic and Evolutionary Computation Conference (GECCO 2020)
08 Jul. 2020
08 Jul. 2020- 08 Jul. 2020

Directionality Reinforcement Learning to Operate Multi-Agent System without Communication
Fumito Uwano; Keiki Takadama
English, The 11th International Workshop on Optimization and Learning in Multiagent Systems (OptLearnMAS2020), The 19th International Joint on Autonomous and Multi-agent systems(AAMAS 2020), Peer-reviewed
May 2020
May 2020 May 2020

Non-contact Sleep Apnea Syndrome Detection Based on What Random Forests Learned,
Nakari, I; Kitajima, E; Tajima, Y; Takadama, K
English, 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech 2020)
11 Mar. 2020
11 Mar. 2020- 11 Mar. 2020

個別最適行動からの行動探査によるアーカイブを活用した協調型マルチエージェント逆強化学習
福本 有季子; 長谷川 智; 田所 優和; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第47回知能システムシンポジウム, 計測自動制御学会，第47回知能システムシンポジウム, 愛知県，名古屋市, Domestic conference
02 Mar. 2020

マルチエージェント強化学習による目的数の異なるエージェント間の目的推定
坂本 充生; 前川 佳幹; 北島 瑛貴; 上野 史; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第47回知能システムシンポジウム, 計測自動制御学会, 愛知県，名古屋市, Domestic conference
02 Mar. 2020

多目的意思決定支援のためのパレートフロントの上位集合の獲得に関する検討
高木 智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第17回進化計算学会研究会, 進化計算学会, 東京都，調布市, Domestic conference
28 Feb. 2020

小型月着陸実証機SLIMの開発状況
坂井 真一郎; 櫛木 賢一; 澤井 秀次郎; 福田 盛介; 荒川 哲人; 齋藤 宏生; 佐藤 英一; 上野 誠也; 鎌田 弘之; 北薗 幸一; 小島 広久; 下地 治彦; 高玉 圭樹; 能見 公博; 樋口 丈浩; SLIMプロジェクトチーム
Oral presentation, Japanese, 第20回 宇宙科学シンポジウム (SSS 2020), 神奈川県，相模原市, Domestic conference
08 Jan. 2020

分解型多数目的最適化における指向確率選択と二重連鎖更新の効果
佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第13回進化計算シンポジウム 2019, 進化計算学会, 淡路島，兵庫県, Domestic conference
15 Dec. 2019

競争群最適化における比較群サイズが最適化性能に与える影響
三好 陵太; 高木 智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第13回進化計算シンポジウム 2019, 進化計算学会, 淡路島，兵庫県, Domestic conference
15 Dec. 2019

局所収束回避に向けた粒子群最適化と差分進化の空間的探索戦略の切り替え
河野 航大; 梶原 奨; 小林 亮太; 高野 諒; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第13回進化計算シンポジウム 2019, 進化計算学会, 淡路島，兵庫県, Domestic conference
15 Dec. 2019

Local Covering: 保持分類子の条件部一般化に基づく XCS の適応的ルール生成法
田所 優和; 長谷川 智; 辰巳 嵩豊; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第13回進化計算シンポジウム 2019, 進化計算学会, 淡路島，兵庫県, Domestic conference
14 Dec. 2019

非劣解サンプリングのための多目的進化計算における環境選択法の比較評価
高木 智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第13回進化計算シンポジウム 2019, 進化計算学会, 淡路島，兵庫県, Domestic conference
14 Dec. 2019

評価値軸・設計変数上の解の継続変化に対する群知能アルゴリズムのメカニズムの設計とその追従性評価
高野 諒; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第13回進化計算シンポジウム 2019, 進化計算学会, 淡路島，兵庫県, Domestic conference
14 Dec. 2019

格子型多数目的進化アルゴリズムにおける分解粒度の適応的決定に関する検討
加納 謙介; 高木 智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第13回進化計算シンポジウム 2019, 進化計算学会, 淡路島，兵庫県, Domestic conference
14 Dec. 2019

多目的最適化のベンチマーク問題マップとアルゴリズムマップに関する検討
山本 康平; 高木 智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第13回進化計算シンポジウム 2019, 進化計算学会, 淡路島，兵庫県, Domestic conference
14 Dec. 2019

多因子距離最小化問題における進化計算の性能比較
川上 紫央; 高木 智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第13回進化計算シンポジウム 2019, 進化計算学会, 淡路島，兵庫県, Domestic conference
14 Dec. 2019

不確実性を伴うデータを分類するルール獲得に向けた正確性によるルール選択メカニズムの設計
辰巳嵩豊; 佐藤寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第13回進化計算シンポジウム 2019, 進化計算学会, 淡路島，兵庫県, Domestic conference
14 Dec. 2019

Sleep Apnea Syndrome Detection based on Biological Vibration Data from Mattress Sensor
Nakari, I; Murata, A; Kitajima, E; Sato, H; Takadama, K
Oral presentation, English, The 2019 IEEE Symposium Series on Computational Intelligence(SSCI 2019), Peer-reviewed, International conference
06 Dec. 2019

Simultaneous Local Adaptation for Different Local Properties
Kobayashi, R; Takano, R; Sato, H; Takadama, K
Oral presentation, English, The 22nd Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2019), Tottori, Tottori, Peer-reviewed, International conference
06 Dec. 2019

多次元意見共有エージェントネットワークモデルにおける複数の環境情報発信源を考慮した誤報伝搬防止アルゴリズム
北島 瑛貴; 村田 暁紀; 上野 史; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2019 (SSI2019), 計測自動制御学会, 千葉市，千葉県, Domestic conference
25 Nov. 2019

行動系列分割に基づく不完全なエキスパートからの逆強化学習
長谷川 智; 上野 史; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2019 (SSI2019), 千葉市，千葉県, Domestic conference
24 Nov. 2019

Towards Adaptation to Environmental Change without Network Revision in Urban Transit Network Design Problem
Kajihara, S; Kobayashi, R; Takano, R; Takadama, K; Aratani, T; Majima, T
Oral presentation, English, The 3rd International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2019), akunigami-gun, Okinawa, Peer-reviewed
20 Nov. 2019

Toward Tracking to Serial Movement for Two Swarm Intelligence Algorithms in Dynamic Environments
Takano, R; Takadama, K
Oral presentation, English, The 3rd International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2019), Kunigami-gun，Okinawa, Peer-reviewed, International conference
20 Nov. 2019

画像航行への応用を想定した外乱を伴う撮影画像と地図画像との高精度マッチングに関する研究
小原 静華; 小松原 燿介; 津田 啓輔; 三木 健生; 鎌田 弘之; 高玉 圭樹; 石田 貴行; 福田 盛介; 澤井 秀次郎; 坂井 真一郎
Oral presentation, Japanese, 第63回宇宙科学技術連合講演会, JSASS-2019-4020, 日本航空宇宙学会, 徳島市、徳島県, Domestic conference
06 Nov. 2019

クレータの座標ずれを利用したSLIM探査機の自己位置推定精度の向上
藁谷 由香; 上野 史; 高玉 圭樹; 鎌田 弘之; 石田 貴行; 福田 盛介; 澤井 秀次郎; 坂井 真一郎
Oral presentation, Japanese, 第63回宇宙科学技術連合講演会, JSASS-2019-4018, 日本航空宇宙学会, 徳島市，徳島県, Domestic conference
06 Nov. 2019

覚醒時における睡眠時無呼吸症候群患者の生体振動データの時系列分析
中理 怡恒; 田島 友祐; 高玉 圭樹
Oral presentation, Japanese, 第3回 ヘルスケア・医療情報通信技術研究会 (MICT)，電子情報通信学会, 信学技報, MICT2019--32, つくば市，茨城県, Domestic conference
05 Nov. 2019

逆強化学習における準最適行動系列からの最適行動獲得に向けたエキスパート行動の修正
Satoshi Hasegawa; Fumito Uwano; Keiki Takadama
Japanese, Proceedings of SICE SSI 2019
Nov. 2019
Nov. 2019 Nov. 2019

Application of Multi Agent System and Transition Matrix Analysis to Logistics System for Equal Distribution under Disaster Situation
Majima, T; Takadama, K; Watanabe, D; Aratani, T; Sato, K
Oral presentation, English, SICE Annual Conference 2019, 広島市, 広島県, Peer-reviewed, International conference
11 Sep. 2019

エージェント間通信を伴わず環境状態および報酬の包括的動的変化に追従する理論的マルチエージェント強化学習
上野 史; 高玉 圭樹
Oral presentation, Japanese, JAWS2019 (Joint Agent Workshops and Symposium), 別府市，大分県, Domestic conference
10 Sep. 2019

重みベクトルの部分集合選択による進化型多目的最適化に関する基礎検討
高木 智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 電気学会，電子・情報・システム部門大会, 電気学会, 中頭郡（沖縄）
05 Sep. 2019

非通信マルチエージェント強化学習における獲得報酬値の変動を用いたエージェント数の動的変化への追従
上野 史; 高玉 圭樹
Oral presentation, Japanese, Forum on Information Technology: FIT 2019, 岡山市，岡山県, Domestic conference
03 Sep. 2019

How to Design Adaptable Agents to Obtain Consensus with Omoiyari
Maekawa, Y; Uwano, F; Kitajima, E; Takadama, K
Oral presentation, English, The 21st International Conference on Human-Computer Interaction (HCI International 2019), Florida, USA, Peer-reviewed, International conference
26 Jul. 2019

Model-based Multi-Objective Reinforcement Learning with Unknown Weights
Yamaguchi, T; Nagahama, S; Ichikawa, Y; Takadama, K
Oral presentation, English, The 21st International Conference on Human-Computer Interaction (HCI International 2019), Florida, USA, Peer-reviewed, International conference
26 Jul. 2019

XCS-CR for Handling Input, Output, and Reward Noise
Tatsumi, T; Takadama, K
Oral presentation, English, International Workshop on Learning Classifier Systems (IWLCS 2019) in Genetic and Evolutionary Computation Conference (GECCO 2019), Prague, Czech Republic, Peer-reviewed, International conference
13 Jul. 2019

How to Select Appropriate Craters to Estimate Location Accurately in Comprehensive Situations for SLIM Project
Uwano, F; Tatsumi, T; Murata, A; Takadama, K; Kamata, H; Ishida, T; Fukuda S; Sawai, S; Sakai, S
Oral presentation, English, The 32nd International Symposium on Space Technology and Science (ISTS 2019) & 9th Nano-Satellite Symposium (NSAT), Fukui, Fukui, Peer-reviewed, International conference
20 Jun. 2019

Complex-Valued-based Learning Classifier System for POMDP Environments
Takadama, K; Yamazaki, D; Nakata, M; H. Sato
Oral presentation, English, 2019 IEEE Congress on Evolutionary Computation (CEC2019), Wellington, New Zealand, Peer-reviewed, International conference
12 Jun. 2019

Comparison of Statistical Table- and Non-Statistical Table-based XCS in Noisy Environments
Tatsumi, T; Takadama, K
Oral presentation, English, 2019 IEEE Congress on Evolutionary Computation (CEC2019), Wellington, New Zealand, Peer-reviewed, International conference
12 Jun. 2019

Knowledge Extraction from XCSR Based on Dimensionality Reduction and Deep Generative Models
Tadokoro, M; Hasegawa, S; Tatsumi, T; Sato, H; Takadama, K
Oral presentation, English, 2019 IEEE Congress on Evolutionary Computation (CEC2019), Wellington, New Zealand, Peer-reviewed, International conference
12 Jun. 2019

Niche Radius Adaptation in Bat Algorithm for Locating Multiple Optima in Multimodal Functions
Iwase, T; Takano, R; Uwano, F; Sato, H; Takadama, K
Oral presentation, English, 2019 IEEE Congress on Evolutionary Computation (CEC2019), Wellington, New Zealand, Peer-reviewed, International conference
11 Jun. 2019

Complex-Valued-based Learning Classifier System for POMDP Environments
Takadama, K; Yamazaki, D; Nakata, M; H. Sato
Oral presentation, English, 2019 IEEE Congress on Evolutionary Computation (CEC2019), Wellington, New Zealand, Peer-reviewed, International conference
11 Jun. 2019

Comparison of Statistical Table- and Non-Statistical Table-based XCS in Noisy Environments
Tatsumi, T; Takadama, K
Oral presentation, English, 2019 IEEE Congress on Evolutionary Computation (CEC2019), Wellington, New Zealand, Peer-reviewed, International conference
10 Jun. 2019

Knowledge Extraction from XCSR Based on Dimensionality Reduction and Deep Generative Models
Tadokoro, M; Hasegawa, S; Tatsumi, T; Sato, H; Takadama, K
Oral presentation, English, 2019 IEEE Congress on Evolutionary Computation (CEC2019), Wellington, New Zealand, Peer-reviewed, International conference
10 Jun. 2019

北米便に対する上空通過機と日本出発便のモデリング
村田 暁紀; 高玉 圭樹; マーク ブラウン; 平林 博子; 虎谷 大地
Oral presentation, Japanese, 平成31年度 電子航法研究所研究発表会(第19回), 調布, 東京, Domestic conference
07 Jun. 2019

集団適応を導くギャップ補填に基づく「思いやり」
前川 佳幹; 上野 史; 北島 瑛貴; 高玉 圭樹
Oral presentation, Japanese, 2019年度人工知能学会全国大会(第33回), 新潟市，新潟県, Domestic conference
06 Jun. 2019

無拘束型リアルタイム睡眠段階推定と深い睡眠を導く行動マイニング
高玉 圭樹
Oral presentation, Japanese, 新技術説明会，JST(科学技術支援機構), Invited, 千代田区，東京都, Domestic conference
14 May 2019

Sleep Stage Estimation using Heart Rate Variability divided by Sleep Cycle with Relative Evaluation
Tobaru, A; Tajima, Y; Takadama, K
Oral presentation, English, The AAAI 2019 Spring Symposia, Interpretable AI for Well-Being: Understanding Cognitive Bias and Social Embeddedness, Palo Alto, USA, Peer-reviewed, International conference
27 Mar. 2019

Toward Good Circadian Rhythm through an valuate of Stress Condition
Takano, R; Kajihara, S; Hasegawa, S; Kitajima, E; Takadama, K; Shimuta, T; Yabe, T; Matsumoto, H
Oral presentation, English, The AAAI 2019 Spring Symposia, Interpretable AI for Well-Being: Understanding Cognitive Bias and Social Embeddedness, Palo Alto, USA, Peer-reviewed, International conference
27 Mar. 2019

WAKE Detection During Sleep using Random Forest for Apnea Syndrome Patients
Nakari, I; Tajima, Y; Takano, R; Tobaru, A; Takadama, K
Oral presentation, English, The AAAI 2019 Spring Symposia, Interpretable AI for Well-Being: Understanding Cognitive Bias and Social Embeddedness, Palo Alto, USA, Peer-reviewed, International conference
27 Mar. 2019

The Challenges for Interpretable AI for Well-being
Kido, T; Takadama, K
Oral presentation, English, The AAAI 2019 Spring Symposia, Interpretable AI for Well-Being: Understanding Cognitive Bias and Social Embeddedness, Palo Alto, USA, Peer-reviewed, International conference
25 Mar. 2019

What Makes It Difficult To Apply AI Into Well-being and Its Solution: An Example of Sleep Apnea Syndrome
Takadama, K
Oral presentation, English, The AAAI 2019 Spring Symposia, Interpretable AI for Well-Being: Understanding Cognitive Bias and Social Embeddedness, Palo Alto, USA, Peer-reviewed, International conference
25 Mar. 2019

Bat Algorithm with Dynamic Niche Radius for Multimodal Optimization
Iwase, T; Takano, R; Uwano, F; Sato, H; Takadama, K
Oral presentation, English, The 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI 2019), Male, Maldives, Peer-reviewed, International conference
24 Mar. 2019

A Distribution Control of Weight Vector Set for Multi-objective Evolutionary Algorithms
Takagi, T; Takadama,K; Sato, H
Oral presentation, English, The 11th EAI International Conference on Bio-inspired Information and Communications Technologies (BICT 2019), Pittsburgh, USA, Peer-reviewed, International conference
13 Mar. 2019

好奇心を持つエージェントによる多様性のある情報伝搬シミュレーションモデルの提案
北島 瑛貴; 上野 史; 村田 暁紀; 高玉 圭樹
Oral presentation, Japanese, 人工知能学会， HAIシンポジウム2018, 神奈川県，川崎市, Domestic conference
08 Mar. 2019

バス路線網における運行形態の一般化による環境変化への適応
梶原 奨; 村田 暁紀; 長谷川 智; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, 第46回知能システムシンポジウム, 滋賀県，大津市
07 Mar. 2019

Random Forestを用いた無拘束型生体センサによる睡眠時無呼吸症候群の判別と中途覚醒推定
中理 怡恒; 田島 友祐; 桃原 明里; 北島 瑛貴; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, 第46回知能システムシンポジウム, 滋賀県，大津市, Domestic conference
07 Mar. 2019

故障に対して冗長性を備えた仮想ロボットのニューロ進化による持続可能な行動獲得
速水 陽平; 上野 史; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, 第46回知能システムシンポジウム, 滋賀県，大津市, Domestic conference
07 Mar. 2019

故障に対して冗長性を備えた仮想ロボットのニューロ進化による持続可能な行動獲得
速水陽平; 辰巳嵩豊; 上野史; 高玉圭樹
Japanese, 知能システムシンポジウム講演資料(CD-ROM), http://jglobal.jst.go.jp/public/201902218229025617
06 Mar. 2019
06 Mar. 2019- 06 Mar. 2019

大脳新皮質学習における適応型シナプス配置法の検討
青木 健; 鈴ヶ嶺 聡哲; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会, 第46回知能システムシンポジウム, 滋賀県，大津市, Domestic conference
06 Mar. 2019

自己構成型大脳新皮質学習における時間遅れシナプスの検討
鈴ヶ嶺 聡哲; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会, 第46回知能システムシンポジウム, 滋賀県，大津市, Domestic conference
06 Mar. 2019

An Improved Social-Spider Optimization Algorithm for Tracking Peak in Dynamic Environments
Takano, R; Takadama, K
Oral presentation, English, The 24th International Symposium on Artificial Life and Robotics (AROB 2019), Beppu, Oita, Peer-reviewed, International conference
24 Jan. 2019

Maximum Entropy Inverse Reinforcement Learning with incomplete expert
Hasegawa, S; Uwano, F; Takadama, K
Oral presentation, English, The 24th International Symposium on Artificial Life and Robotics (AROB 2019), Beppu, Oita, Peer-reviewed, International conference
23 Jan. 2019

小型月着陸実証機SLIMの開発状況
坂井 真一郎; 櫛木 賢一; 澤井 秀次郎; 福田 盛介; 荒川 哲人; 齋藤宏生; 佐藤 英一; 上野 誠也; 鎌田 弘之; 北薗 幸一; 小島 広久; 下地 治彦; 高玉 圭樹; 能見 公博; 樋口 丈浩; SLIMプロジェクトチーム
Oral presentation, Japanese, 第19回 宇宙科学シンポジウム (SSS 2019), 神奈川県，相模原市, Domestic conference
09 Jan. 2019

高精度月着陸のための画像航法および自律誘導制御
石田 貴行; 植田 聡史; 伊藤 琢博; 坂井 真一郎; 福田盛介; 上野 誠也; 樋口 丈浩; 鎌田 弘之; 高玉 圭樹; 小島 広久; 狩谷 和季
Oral presentation, Japanese, 第19回 宇宙科学シンポジウム (SSS 2019), 神奈川県，相模原市, Domestic conference
09 Jan. 2019

Novelty Search-based Bat Algorithm: Adjusting Distance among Solutions for Multimodal Optimization
Iwase, T; Takano, R; Uwano, F; Sato, H; Takadama, K
Oral presentation, English, The 22nd Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2018), Sapporo, Hokkaido, Peer-reviewed, International conference
20 Dec. 2018

分解に基づく多目的進化計算における重みベクトル群の分布制御に関する検討
高木 智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第12回進化計算シンポジウム 2018, 福岡市，福岡県, Domestic conference
08 Dec. 2018

動的最適化問題に向けた異種戦略の個体別適用に基づく差分進化
小林 亮太; 岩瀬 拓哉; 高野 諒; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第12回進化計算シンポジウム 2018, 福岡市，福岡県, Domestic conference
08 Dec. 2018

解釈可能なルール獲得に向けた深層生成モデルによる次元削減に基づく学習分類子システム
田所 優和; 速水 陽平; 藤野 貴章; 辰巳 嵩豊; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第12回進化計算シンポジウム 2018, 福岡市，福岡県, Domestic conference
08 Dec. 2018

Social Spider Optimization Algorithmの動的最適化問題への適用：変化への追従から先回りへ
高野 諒; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第12回進化計算シンポジウム 2018, 福岡市，福岡県, Domestic conference
08 Dec. 2018

多目的最適化問題群マップにおける未知問題の位置推定法の検討
山本 康平; 宮川 みなみ; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会, 第12回進化計算シンポジウム 2018, 福岡市，福岡県, Domestic conference
08 Dec. 2018

Dynamic Niche Radiusに基づく個体間距離を考慮したBat Algorithm
岩瀬 拓哉; 高野 諒; 上野 史; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会, 進化計算シンポコンペティション 2018，第7位, Domestic conference
08 Dec. 2018

A Study on a Cortical Learning Algorithm Dynamically Adjusting Columns and Cells
Suzugamine, S; Aoki, T; Takadama, K; Sato, H
Oral presentation, English, Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2018), 富山県富山市, Peer-reviewed, International conference
06 Dec. 2018

XCS for Missing Attributes in Data
Tatsumi, T; Takadama, K
Oral presentation, English, Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems (SCIS & ISIS2018), 富山県富山市, Peer-reviewed, International conference
06 Dec. 2018

高齢化社会におけるビッグデータと人工知能の役割
高玉 圭樹
Invited oral presentation, Japanese, 順天堂大学，大学院特別講義, Invited, 順天堂大学
28 Nov. 2018

複数解探索を考慮した分散型Bat Algorithm
岩瀬拓哉; 高野諒; 上野史; 佐藤寛之; 高玉圭樹
Japanese, 計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM), http://jglobal.jst.go.jp/public/201902247291836692
25 Nov. 2018
25 Nov. 2018- 25 Nov. 2018

報酬の動的変化に適応する通信なしマルチエージェント協調学習のための公平性に基づく内部報酬設定法
上野史; 高玉圭樹
Japanese, 計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM), http://jglobal.jst.go.jp/public/201902226549519292
25 Nov. 2018
25 Nov. 2018- 25 Nov. 2018

実行不可能解における遺伝子内の最大制約違反量と平均制約違反量を活用した解修復法
村田 暁紀; Daniel Delahaye; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2018 (SSI 2018), 富山市，富山県, Domestic conference
25 Nov. 2018

グリッドネットワーク上の誤報抑制意見共有アルゴリズム
北島瑛貴; 辰巳 嵩豊; 村田 暁紀; 上野 史; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2018 (SSI 2018), 富山市，富山県, Domestic conference
25 Nov. 2018

複数解探索を考慮した分散型 Bat Algorithm
岩瀬 拓哉; 高野 諒; 上野 史; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2018 (SSI 2018), 富山市，富山県), Domestic conference
25 Nov. 2018

報酬の動的変化に適応する通信なしマルチエージェント協調学習のための 公平性に基づく内部報酬設定法
上野 史; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2018 (SSI 2018), 富山市，富山県, Domestic conference
25 Nov. 2018

大脳新皮質学習におけるシナプスの動的再配置に関する検討
青木 健; 鈴ヶ嶺 聡哲; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2018 (SSI 2018), 富山市，富山県, Domestic conference
25 Nov. 2018

大脳新皮質学習におけるカラムとセルの動的構成法の動作解析
鈴ヶ嶺 聡哲; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2018 (SSI 2018), 富山市，富山県, Domestic conference
25 Nov. 2018

最適化問題群マップにおける未知問題の位置推定に関する基礎検討
山本康平; 宮川みなみ; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2018 (SSI 2018), 富山市，富山県, Domestic conference
25 Nov. 2018

行動系列分割に基づく不完全なエキスパートからの逆強化学習
長谷川 智; 上野 史; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2018 (SSI 2018), 富山市，富山県, Domestic conference
25 Nov. 2018

多次元意見共有エージェントネットワークモデルにおける複数の環境情報
中理 怡恒; 田島 友祐; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2019 (SSI 2019), 千葉市，千葉県, Domestic conference
25 Nov. 2018

Well-being Computing：パーソナルビッグデータからの睡眠状態推定とその展開
高玉 圭樹
Invited oral presentation, Japanese, 第8回賢さの先端研究会, Invited, 計測自動制御学会，知能工学部会／システム工学部会(共催), 富山県富山市, Domestic conference
24 Nov. 2018

いかにマルチエージェントシステムを操るかー人工物から人間まで
高玉 圭樹
Invited oral presentation, Japanese, マルチエージェントシステムの数理とシミュレーション，数理工学ワークショップ, Invited, 大阪大学 数理・データ科学教育研究センター, 大阪府吹田市, Domestic conference
14 Nov. 2018

睡眠時無呼吸症候群患者に対する無拘束型リアルタイム睡眠段階推定法の分析
田島 友祐; 高野 諒; 上野 史; 原田 智広; 高玉 圭樹
Oral presentation, Japanese, 第3回 ヘルスケア・医療情報通信技術研究会 (MICT)，電子情報通信学会，信学技報, MICT2018-46, 兵庫県、神戸市, Domestic conference
06 Nov. 2018

睡眠時無呼吸症候群患者のための無拘束型リアルタイム睡眠段階推定法
Yusuke Tajima; Fumito Uwano; Tomohiro Harada; Keiki Takadama
MICT, https://www.ieice.org/ken/program/index.php?tgs_regid=431d64beb688782bf1a545e5a5488f24682312697241c0d6d0914daddc9445a6&tgid=IEICE-MICT&lang=
Nov. 2018
Nov. 2018 Nov. 2018

睡眠時無呼吸症候群患者に対する無拘束型リアルタイム睡眠段階推定法の分析
田島友祐; 高野諒; 上野史; 原田智広; 高玉圭樹
Japanese, 電子情報通信学会技術研究報告, http://jglobal.jst.go.jp/public/201902210822046685
30 Oct. 2018
30 Oct. 2018- 30 Oct. 2018

包括的な撮影画像パターンに対するSLIM探査機の自己位置推定の評価と精度向上
上野 史; 村田 暁紀; 辰巳 嵩豊; 高 圭樹; 鎌田 弘之; 石田 貴行; 福田 盛介; 澤井 秀次郎; 坂井 真一郎
Oral presentation, Japanese, 第62回宇宙科学技術連合講演会，日本航空宇宙学会, 日本航空宇宙学会, 久留米市，福岡県, Domestic conference
24 Oct. 2018

月面画像に基づくクレータ検出と特徴点検出法によるバックアップ処理について
小松原 燿介; 上沼 大悟; 津田 啓輔; 三木 健生; 上原 あかり; 鎌田 弘之; 高玉 圭樹; 石田 貴行; 福田 盛介; 澤井 秀次郎; 坂井 真一郎
Oral presentation, Japanese, 第62回宇宙科学技術連合講演会，日本航空宇宙学会，JSASS-2018-4088, 福岡県久留米市, Domestic conference
24 Oct. 2018

Transportation Simulator for Disaster Circumstance and Bottleneck Analysis
Majima, T; Takadama, K; Watanabe, D; Aratani, T; Sato, K
English, Journal Artificial Life and Robotics, Springer--Verlag
03 Oct. 2018
03 Oct. 2018- 03 Oct. 2018

Strategy for Learning Cooperative Behavior with Local Information for Multi-agent Systems
Fumito Uwano; Keiki Takadama
English, The 21st International Conference on Principles and Practice of Multi-Agent Systems, Peer-reviewed, http://dblp.uni-trier.de/db/conf/prima/prima2018.html#conf/prima/UwanoT18
Oct. 2018
Oct. 2018 Oct. 2018

航空機着陸問題におけるクラスタリングを用いた分割反復最適化手法
村田 暁紀; 佐藤 寛之; 高玉圭 樹; Daniel Delahaye
Oral presentation, Japanese, 電気学会，第28回インテリジェント・システム・シンポジウム, 電気学会, 横浜，神奈川県, Domestic conference
27 Sep. 2018

多目的進化計算における重みベクトルの分布に関する基礎検討
高木 智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 電気学会，第28回インテリジェント・システム・シンポジウム, 電気学会, 横浜，神奈川県, Domestic conference
26 Sep. 2018

A Preliminary Study on Weight Vector Distribution Control Based on Intermediate Objective Value
Takagi, T; Takadama, K; Sato, H
Oral presentation, English, 2018 JPNSEC International Workshop on Evolutionary Computation, JPNSEC International Workshop on Evolutionary Computation, Shenzhen, China, Peer-reviewed, International conference
31 Aug. 2018

Multiple Swarm Intelligence Methods based on Multiple Population with Sharing Best Solution for Drastic Environmental Change
Umenai, Y; Uwano, F; Sato, H; Takadama, K
Oral presentation, English, Genetic and Evolutionary Computation Conference (GECCO 2018), kyoto, Peer-reviewed, International conference
17 Jul. 2018

Artificial Bee Colony Algorithm based on Adaptive Local Information Sharing: Approach for several dynamic changes
Takano, R; Sato, H; Takadama, K
Oral presentation, English, Genetic and Evolutionary Computation Conference (GECCO 2018), kyoto, Peer-reviewed, International conference
17 Jul. 2018

Generalizing Rules by Random Forest-based Learning Classifier Systems for High-Dimensional Data Mining
Uwano, F; Dobashi, K; Takadama, K
Oral presentation, English, International Workshop on Learning Classifier Systems (IWLCS 2018) in Genetic and Evolutionary Computation Conference (GECCO 2018), kyoto, Peer-reviewed, International conference
16 Jul. 2018

XCS-CR: Determining Accuracy of Classifier by its Collective Reward in Action Set toward Environment with Action Noise
Tatsumi, T; Kovacs, T; Takadama, K
Oral presentation, English, International Workshop on Learning Classifier Systems (IWLCS 2018) in Genetic and Evolutionary Computation Conference (GECCO 2018), Kyoto, Peer-reviewed, International conference
16 Jul. 2018

XCSR Based on Compressed Input by Deep Neural Network for High Dimensional Data
Matsumoto, K; Takano, R; Tatsumi, T; Sato, H; Kovacs, T; Takadama, K
Oral presentation, English, International Workshop on Learning Classifier Systems (IWLCS 2018) in Genetic and Evolutionary Computation Conference (GECCO 2018), kyoto, Peer-reviewed, International conference
16 Jul. 2018

Classifier Generalization for Comprehensive Classifiers Subsumption in XCS
Zhang, C; Tatsumi, T; Sato, H; Kovacs, T; Takadama, K
Oral presentation, English, Evolutionary Computation in Health care and Nursing System in Genetic and Evolutionary Computation Conference (GECCO 2018), kyoto, Peer-reviewed, International conference
15 Jul. 2018

Multiple swarm intelligence methods based on multiple population with sharing best solution for drastic environmental change.
Yuta Umenai; Fumito Uwano; Hiroyuki Sato; Keiki Takadama
The Genetic and Evolutionary Computation Conference (GECCO 2018), Companion, ACM, http://doi.acm.org/10.1145/3205651.3205800
Jul. 2018
Jul. 2018 Jul. 2018

Generalizing rules by random forest-based learning classifier systems for high-dimensional data mining.
Fumito Uwano; Koji Dobashi; Keiki Takadama; Tim Kovacs
The Genetic and Evolutionary Computation Conference (GECCO 2018), Companion, ACM, http://doi.acm.org/10.1145/3205651.3208298
Jul. 2018
Jul. 2018 Jul. 2018

Merging Flows and Optimizing Aircraft Scheduling in Terminal Maneuvering Area Based on GA
Murata, A; Delahaye, D; Takadama, K
Oral presentation, English, International Conference for Research in Air Transportation (ICRAT 2018), Barcelon, Spain, Peer-reviewed, International conference
27 Jun. 2018

自己構成型の大脳新皮質学習アルゴリズムに関する検討
鈴ヶ嶺 聡哲; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第13回コンピューテーショナル・インテリジェンス研究会, 八王子，東京都, Domestic conference
17 Jun. 2018

自己構成型の大脳新皮質学習アルゴリズムに関する検討
鈴ヶ嶺 聡哲; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会（SICE），システム・情報部門， 第13回コンピューテーショナル・インテリジェンス研究会, 計測自動制御学会（SICE），システム・情報部門，, 八王子，東京都, Domestic conference
16 Jun. 2018

健康促進に向けたサーカディアンリズムに着目した睡眠とストレスの分析
高野 諒; 長谷川 智; 梅内 祐太; 辰巳 嵩豊; 高玉 圭樹; 志牟 田亨; 家邉 徹; 松本 英雄
Oral presentation, Japanese, 2018年度人工知能学会全国大会(第32回)2F2-OS-4a-04, Domestic conference
06 Jun. 2018

How to Detect Essential Craters in Camera Shot Image for Increasing the Number of Spacecraft Location Candidates while Improving Its Estimation Accuracy?
Ishii, H; Umenai, Y; Matsumoto, K; Uwano, F; Tatsumi, T; Takadama, K; Kamata, H; Ishida, T; Fukuda S; Sawai, S; Sakai, S
Oral presentation, English, The 14th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS2018), Madrid, Spain, Peer-reviewed, International conference
05 Jun. 2018

Analyzing Triangle Matching Method Based on Craters for Spacecraft Localization
Uwano, F; Ishii, H; Umenai, Y; Matsumoto, K; Tatsumi, T; Murata, A; Takadama, K
Oral presentation, English, The 14th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS2018), Madrid, Spain, Peer-reviewed, International conference
05 Jun. 2018

Sleep Stage Re-Estimation Method focus on Changing Sleep Cycles
Tajima, Y; Murata, A; Harada, T; Takadama, K
Oral presentation, English, The AAAI 2018 Spring Symposia,Beyond Machine Intelligence: Understanding Cognitive Bias and Humanity for Well-being AI, AAAI (The Association for the Advancement of Artificial Intelligence), Stanford, USA, Peer-reviewed, International conference
28 Mar. 2018

Improving Sleep Stage Estimation Accuracy by Circadian Rhythm Extracted from a Low Frequency component of Heart rate
Tobaru, A; Uwano, F; Iwase, T; Matsumoto, K; Takano, R; Tajima; Y. Umenai, Y; Takadama, K
Oral presentation, English, AAAI (The Association for the Advancement of Artificial Intelligence), The AAAI 2018 Spring Symposia,Beyond Machine Intelligence: Understanding Cognitive Bias and Humanity for Well-being AI, Stanford, USA, Peer-reviewed, International conference
28 Mar. 2018

Ensemble Method for Heart Rate Extraction from Pressure Sensor Data
Uwano, F; Takadama, K
Oral presentation, English, The AAAI 2018 Spring Symposia,Beyond Machine Intelligence: Understanding Cognitive Bias and Humanity for Well-being AI, AAAI (The Association for the Advancement of Artificial Intelligence), Stanford, USA, Peer-reviewed, International conference
28 Mar. 2018

Study of Analytical Methods on the Relationship Between Sleep Quality and Stress With a Focus on Human Circadian Rhythm
Takano, R; Umenai, Y; Tastumi, T; Takadama, K
Oral presentation, English, The AAAI 2018 Spring Symposia,Beyond Machine Intelligence: Understanding Cognitive Bias and Humanity for Well-being AI, AAAI (The Association for the Advancement of Artificial Intelligence), Stanford, USA, Peer-reviewed, International conference
26 Mar. 2018

Can Machine Learning Correct Commonly Accepted Knowledge and Provide Understandable Knowledge in Care Support Domain? -- Tackling Cognitive Bias and Humanity from Machine Learning Perspective --
Takadama, K
Oral presentation, English, The AAAI 2018 Spring Symposia, Beyond Machine Intelligence: Understanding Cognitive Bias and Humanity for Well-being AI, AAAI (The Association for the Advancement of Artificial Intelligence), Stanford, USA, Peer-reviewed, International conference
26 Mar. 2018

The Challenges for Understanding Cognitive Bias and Humanity for Well-being AI -- Beyond Machine Intelligence --
Kido, T; Takadama, K
Oral presentation, English, The AAAI 2018 Spring Symposia, Beyond Machine Intelligence: Understanding Cognitive Bias and Humanity for Well-being AI, AAAI (The Association for the Advancement of Artificial Intelligence), Stanford, USA, Peer-reviewed, International conference
26 Mar. 2018

報酬生起確率ベクトルに基づくあらゆる状況に対する強化学習
長濵 将太; 市川 嘉裕; 高玉 圭樹; 山口 智浩
Oral presentation, Japanese, 計測自動制御学会，第45回知能システムシンポジウム, 大阪，豊中市, Domestic conference
08 Mar. 2018

帰宅困難者の滞留解消に向けた区間混雑に基づく路線間バス譲渡
高谷 美穂; 石井 晴之; 張 財立; 辰巳 嵩豊; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, 第45回知能システムシンポジウム, 大阪，豊中市, Domestic conference
08 Mar. 2018

負の報酬生成による環境変化に適応可能な逆強化学習
長谷川 智; 梅内 祐太; 上野 史; 佐藤 寛之; 山口 智浩; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第45回知能システムシンポジウム, 大阪，豊中市, Domestic conference
08 Mar. 2018

負の報酬生成による環境変化に適応可能な逆強化学習
長谷川智; 梅内祐太; 上野史; 佐藤寛之; 高玉圭樹; 山口智浩
Japanese, 知能システムシンポジウム講演資料(CD-ROM), http://jglobal.jst.go.jp/public/201802262414668286
07 Mar. 2018
07 Mar. 2018- 07 Mar. 2018

帰宅困難者の滞留解消に向けた区間混雑に基づく路線間バス譲渡
高谷美穂; 石井晴之; ZHANG C; 辰巳嵩豊; 佐藤寛之; 高玉圭樹
Japanese, 知能システムシンポジウム講演資料(CD-ROM), http://jglobal.jst.go.jp/public/201802214800585203
07 Mar. 2018
07 Mar. 2018- 07 Mar. 2018

全体構想：医学と情報学との新しい融合とその可能性
高玉 圭樹
Oral presentation, Japanese, 第16回日本病院総合診療医学会学術総会，日本病院総合診療医学会, 別府，大分, Domestic conference
03 Mar. 2018

大脳新皮質学習におけるカラムとセルの動的構成に関する検討
鈴ヶ嶺 聡哲; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 情報処理学会，第117回数理モデル化と問題解決研究発表会, 指宿，鹿児島, Domestic conference
02 Mar. 2018

Ensemble Heart Rate Extraction Method for Biological Data from Water Pressure Sensor
Fumito Uwano; Keiki Takadama
English, 2018 AAAI Spring Symposium Series, Peer-reviewed, https://www.aaai.org/ocs/index.php/SSS/SSS18/paper/view/17547
Mar. 2018
Mar. 2018 Mar. 2018

Improving Sleep Stage Estimation Accuracy by Circadian Rhythm Extracted from a Low Frequency Component of Heart Rate
Akari Tobaru; Fumito Uwano; Takuya Iwase; Kazuma Matsumoto; Ryo Takano; Yusuke Tajima; Yuta Umenai; Keiki Takadama
English, 2018 AAAI Spring Symposium Series, Peer-reviewed, https://www.aaai.org/ocs/index.php/SSS/SSS18/paper/view/17554
Mar. 2018
Mar. 2018 Mar. 2018

非通信マルチエージェント協調行動学習に向けた目的価値と内部報酬に基づく強化学習
上野 史; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門，第11回関論的システムデザイン調査研究会, 計測自動制御学会，システム・情報部門, 大津市，滋賀県, Domestic conference
21 Jan. 2018

睡眠を通して Well-being を見つめよう
田島 友祐; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門，第11回関論的システムデザイン調査研究会, 計測自動制御学会，システム・情報部門, 大津市，滋賀県, Domestic conference
21 Jan. 2018

小型月着陸実証機SLIMの開発状況
坂井 真一郎; 櫛木 賢一; 澤井 秀次郎; 福田 盛介; 佐藤 英一; 上野 誠也; 鎌田 弘之; 北薗 幸一; 小島 広久; 下地 治彦; 高玉 圭樹; 能見 公博; 樋口 丈浩; SLIMプロジェクトチーム
Oral presentation, Japanese, 第18回 宇宙科学シンポジウム (SSS 2018), 相模原，神奈川, Domestic conference
10 Jan. 2018

ピンポイント月着陸のための画像航法および自律誘導制御
植田 聡史; 伊藤 琢博; 坂井 真一郎; 石田 貴行; 福田盛介; 上野 誠也; 樋口 丈浩; 鎌田 弘之; 高玉 圭樹; 小島 広久; 狩谷 和季
Oral presentation, Japanese, 第18回 宇宙科学シンポジウム (SSS 2018), 相模原，神奈川, Domestic conference
10 Jan. 2018

Classifier generalization for comprehensive classifiers subsumption in XCS.
Caili Zhang; Takato Tatsumi; Hiyoyuki Sato; Tim Kovacs; Keiki Takadama
Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, Peer-reviewed, http://doi.acm.org/10.1145/3205651.3208260
2018
2018 2018

XCS-CR: determining accuracy of classifier by its collective reward in action set toward environment with action noise.
Takato Tatsumi; Tim Kovacs; Keiki Takadama
Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, Peer-reviewed, http://doi.acm.org/10.1145/3205651.3208271
2018
2018 2018

XCSR based on compressed input by deep neural network for high dimensional data.
Kazuma Matsumoto; Ryo Takano; Takato Tatsumi; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, Peer-reviewed, http://doi.acm.org/10.1145/3205651.3208281
2018
2018 2018

Strategy for Learning Cooperative Behavior with Local Informationfor Multi-agent Systems
Uwano, F; Takadama, K
Oral presentation, English, The 21st International Conference on Principles and Practice of Multi-Agent Systems (PRIMA2018), Peer-reviewed, International conference
2018

Effects of Chain-Reaction Initial Solution Arrangement in Decomposition-Based MOEAs
Sato, H; Miyakawa, M; Takadama,K
Oral presentation, English, Post Proceedings of Eurosim Congress on Modelling and Simulation (EUROSIM) 2016, Linköping Electronic Conference Proceedings, 2018, Peer-reviewed, International conference
2018

目的数が異なる最適化問題群マップの生成に関する検討
角口 元章; 宮川 みなみ; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第11回進化計算シンポジウム 2017, 函館，北海道, Domestic conference
10 Dec. 2017

Searching multiple local optimal solutions in Multimodal Function by Bat Algorithm based on Novelty Search
Iwase, T; Takano, R; Uwano, F; Umenai, Y; Sato, H; Takadama, K
Oral presentation, English, 進化計算学会，第11回進化計算シンポジウム 2017, 函館，北海道, Domestic conference
10 Dec. 2017

動的環境適応に向けた粒子群最適化とカッコウ探索の協働のための情報共有方法の検討
梅内 祐太; 上野 史; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第11回進化計算シンポジウム 2017, 函館，北海道, Domestic conference
10 Dec. 2017

深層学習による次元圧縮ルールの学習分類子システムにおける初期ルールとしての可能性
松本 和馬; 高野 諒; 上野 史; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第11回進化計算シンポジウム 2017, 函館，北海道, Domestic conference
09 Dec. 2017

多峰性関数におけるランダムな動的環境変化に対する適応的局所情報共有範囲に基づくArtificial Bee Colony アルゴリズムの変化への追従性の検証
高野 諒; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第11回進化計算シンポジウム 2017, Domestic conference
09 Dec. 2017

深層学習による次元圧縮ルールの学習分類子システムにおける初期ルールとしての可能性
松本和馬; 高野諒; 上野史; 佐藤寛之; 高玉圭樹
Japanese, 進化計算シンポジウム講演資料
Dec. 2017
Dec. 2017 Dec. 2017

Searching Multiple Local Optimal Solutions in Multimodal Function by Bat Algorithm based on Novelty Search
Takuya Iwase; Ryo Takano; Fumito Uwano; Yuta Umenai; Haruyuki Ishii; Hiroyuki Sato; Keiki Takadama
Japanese, 進化計算シンポジウム講演資料
Dec. 2017
Dec. 2017 Dec. 2017

Multi-Objetive Optimization Problem Mapping Based on Algorithmic Parameter Rankings
Kakuguchi, M; Miyakawa, M; Takadama, K; Sato, H
Oral presentation, English, The 2017 IEEE Symposium Series on Computational Intelligence (SSCI2017), HAWAII USA, Peer-reviewed, International conference
30 Nov. 2017

大脳新皮質アルゴリズムの簡素化に関する検討
青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2017 (SSI2017), 計測自動制御学会, 浜松，静岡県, Domestic conference
27 Nov. 2017

知識の忘却に基づく迷路形状の変化に追従する非通信マルチエージェント強化学習
上野史; 高玉圭樹
Japanese, 計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM), http://jglobal.jst.go.jp/public/201802242521570164
25 Nov. 2017
25 Nov. 2017- 25 Nov. 2017

データの曖昧性を許容する学習分類子システム:介護データのマイニング
藤野貴章; 辰巳嵩豊; 張財立; 松本和馬; 佐藤寛之; 高玉圭樹
Japanese, 計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM), http://jglobal.jst.go.jp/public/201802224238912172
25 Nov. 2017
25 Nov. 2017- 25 Nov. 2017

連続報酬環境における学習分類子システムの不適切な一般化に関する一考察
辰巳 嵩豊; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2017 (SSI2017), 計測自動制御学会, 浜松，静岡県, Domestic conference
25 Nov. 2017

複数解探索を考慮した分散型Bat Algorithm
岩瀬 拓哉; 高野諒; 上野史; 梅内祐太; 石井晴之; 佐藤寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2017 (SSI2017), 計測自動制御学会, 浜松，静岡県, Domestic conference
25 Nov. 2017

データの曖昧性を許容する学習分類子システム: 介護データのマイニング
藤野 貴章; 辰巳 嵩豊; 張 財立; 松本 和馬; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2017 (SSI2017), 計測自動制御学会, 浜松，静岡県, Domestic conference
25 Nov. 2017

知識の忘却に基づく迷路形状の変化に追従する非通信マルチエージェント
上野 史; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2017 (SSI2017), 計測自動制御学会, 浜松，静岡県, Domestic conference
25 Nov. 2017

報酬生起確率ベクトルと重みベクトルに基づく全ての最適方策の一括強化学習
長濵 将太; 山口 智; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2017 (SSI2017), 計測自動制御学会, 浜松，静岡県, Domestic conference
25 Nov. 2017

限られたデータ数における報酬分散に基づくクラスタリング学習分類子シ ステム：介護プランへの応用
張 財立; 辰巳 嵩豊; 高玉 圭樹
Oral presentation, Japanese, 第60回自動制御連合講演会，計測自動制御学会, 調布，東京, Domestic conference
11 Nov. 2017

The Real-Time Sleep Stage Re-Estimation Method focused on a Sleep Cycle Transition
田島 友祐; 村田 暁紀; 原田 智広; 高玉 圭樹
Japanese, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 電子情報通信学会, http://id.ndl.go.jp/bib/028700009
06 Nov. 2017
06 Nov. 2017- 06 Nov. 2017

睡眠周期の変化に着目したリアルタイム睡眠段階再推定法
田島 友祐; 村田 暁紀; 原田 智広; 高玉 圭樹
Oral presentation, Japanese, 第3回 ヘルスケア・医療情報通信技術研究会 (MICT)，電子情報通信学会，信学技報, MICT2017--34, MI2017-56, 高松，香川県, Domestic conference
06 Nov. 2017

Toward adaptation to various landscape environment by Artificial Bee Colony Algorithm based on Local Information Sharing
Takano, R; Sato, H; Takadama, K
Oral presentation, English, The 2nd International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2017), 京都大学, 京都, Peer-reviewed, International conference
30 Oct. 2017

Transportation Simulator for Disaster Circumstance and Bottleneck Analysis
Majima, T; Takadama, K; Watanabe; D. Taro, A; Sato, K
Oral presentation, English, The 2nd International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2017), 京都大学, 京都, Peer-reviewed, International conference
30 Oct. 2017

SLIM画像照合航法(クレータ検出)
岡田 怜史; 中浜 優佳; 上原 あかり; 鎌田 弘之; 狩谷 和季; 高玉 圭樹; 田貴行; 福田 盛介; 澤井 秀次郎; 坂井 真一郎
Oral presentation, Japanese, 第61回宇宙科学技術連合講演会，日本航空宇宙学会, 新潟市，新潟県, Domestic conference
25 Oct. 2017

SLIM探査機の高度や姿勢の傾きによるクレータ検出位置ずれに対応する自己位置推定法
石井 晴之; 村田 暁紀; 上野 史; 辰巳 嵩豊; 梅内 裕太; 松本 和馬; 高玉 圭樹; 鎌田 弘之; 石田 貴行; 福田 盛介; 澤井 秀次郎; 坂井 真一郎
Oral presentation, Japanese, 第61回宇宙科学技術連合講演会，日本航空宇宙学会, Domestic conference
25 Oct. 2017

適応的局所情報共有範囲に基づくArtificial Bee Colonyアルゴリズムによ る動的多峰性関数最適化
高野 諒; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 第13回進化計算学会研究会，進化計算学会, 草津市，滋賀県, Domestic conference
01 Sep. 2017

進化計算のパラメータランキングに基づく多目的最適化問題群のマッピングに関する検討
角口 元章; 宮川 みなみ; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第13回進化計算学会研究会，進化計算学会, 草津市，滋賀県, Domestic conference
01 Sep. 2017

環境変化に向けたPSOとCuckoo Searchに基づく解集団混合進化計算
梅内 祐太; 上野 史; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 第13回進化計算学会研究会，進化計算学会, 草津市，滋賀県, Domestic conference
01 Sep. 2017

深層学習による圧縮ルールを復元する学習分類子システムとその精度向上
松本 和馬; 高野 諒; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 第13回進化計算学会研究会，進化計算学会, 草津市，滋賀県, Domestic conference
01 Sep. 2017

Strategy to Improve Cuckoo Search toward Adapting Randomly Changing Environment
Umenai, Y; Uwano, F; Sato, H; Takadama, K
Oral presentation, English, The Eighth International Conference on Swarm Intelligence (ICSI 2017), The Eighth International Conference on Swarm Intelligence (ICSI), 博多, 福岡, Peer-reviewed, International conference
27 Jul. 2017

Theoretical XCS Parameter Settings of Learning Accurate Classifiers
Nakata, M; Browne, W; Hamagami, T; Takadama, K
Oral presentation, English, Genetic and Evolutionary Computation Conference (GECCO 2017), Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, German, Peer-reviewed, International conference
17 Jul. 2017

Automatic Adjustment of Selection Pressure based on Range of Reward in Learning Classifier System,
Tatsumi, T; Sato, H; Takadama, K
Oral presentation, English, Genetic and Evolutionary Computation Conference (GECCO 2017), Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, Germany, Peer-reviewed, International conference
17 Jul. 2017

A Study of Self-Adaptive Semi-Asynchronous Evolutionary Algorithm on Multi-Objective Optimization Problem,
Harada, T; Takadama, K
Oral presentation, English, Workshop on Parallel and Distributed Evolutionary Inspired Method,in Genetic and Evolutionary Computation Conference (GECCO 2017), Workshop on Parallel and Distributed Evolutionary Inspired Method,in Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, Germany, Peer-reviewed, International conference
17 Jul. 2017

An Improved MOEA/D Utilizing Variation Angles for Multi-Objective Optimization
Sato, H; Miyakawa, M; Takadama K
Oral presentation, English, Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, Germany, Peer-reviewed, International conference
17 Jul. 2017

快眠音による深睡眠改快眠音による深睡眠改善効果をδパワーで実証
山木 清志; 管野 智; 川嶋 隆宏; 森島 守人; 高玉 圭樹; 原田 智広
Oral presentation, Japanese, 第42回日本睡眠学会シンポジウム, 日本睡眠学会, 横浜・神奈川, Domestic conference
30 Jun. 2017

多目的最適化問題における評価時間の偏りが半非同期進化法に与える影響の分析
原田 智広; 高玉 圭樹
Oral presentation, Japanese, 第11回コンピューテーショナル・インテリジェンス研究会，計測自動制御学会, 大津市・滋賀県, Domestic conference
26 Jun. 2017

Simulation model for emergency medical transportation with deployment of floating medical support systems --- The case study of Tokyo inland earthquake,
Watanabe, D; Majima, T; Takadama, K
Oral presentation, English, International symposium on Scheduling 2017 (ISS 2017), 名古屋, 愛知, Peer-reviewed, International conference
25 Jun. 2017

人工知能の次は何か？ 宇宙システムからヘルスケアまで
高玉 圭樹
Oral presentation, Japanese, 電気通信大学創立100周年記念公開講座, 電気通信大学, 調布 東京, Domestic conference
17 Jun. 2017

Performance Comparison of Parallel Asynchronous Multi-Objective Evolutionary Algorithm with Different Asynchrony,
Harada, T; Takadama, K
Oral presentation, English, 2017 IEEE Congress on Evolutionary Computation (CEC2017), 2017 IEEE Congress on Evolutionary Computation (CEC2017, Donostia - San Sebastian, Spain, Peer-reviewed, International conference
07 Jun. 2017

Applying Variance-based Learning Classifier System without Convergence of Reward Estimation into Various Reward Distribution,
Tatsumi, T; Sato, H; Kovacs, T; Takadama, K
Oral presentation, English, 2017 IEEE Congress on Evolutionary Computation (CEC2017), 2017 IEEE Congress on Evolutionary Computation (CEC2017), Donostia - San Sebastian, Spain, International conference
07 Jun. 2017

The Robust Spacecraft Location Estimation Algorithm Toward The Misdetection Crater and The Undetected Crater in SLIM,
Ishii, H; Murata, A; Uwano, F; Tatsumi, T; Umenai, Y; Matsumoto, K; Takadama, K; Kamata, H; Ishida, T; Fukuda S; Sawai, S; Sakai, S
Oral presentation, English, The 31th International Symposium on Space Technology and Science (ISTS2017), 愛媛, Peer-reviewed, International conference
07 Jun. 2017

心拍数変動の類似性を考慮したリアルタイム睡眠段階推定
田島 友祐; 原田 智広; 高玉 圭樹
Invited oral presentation, Japanese, 2017年度人工知能学会全国大会(第31回), 人工知能学会, 名古屋, Domestic conference
25 May 2017

Well-Being Computing: AIで身体的・心理的・社会的健康を得られるか?
高玉 圭樹
Invited oral presentation, Japanese, 2017年度人工知能学会全国大会(第31回), Invited, 人工知能学会, ウインク愛知 名古屋, Domestic conference
25 May 2017

宇宙機コンピュータシステムの強化に向けたプログラム創発
高玉 圭樹
Invited oral presentation, Japanese, 第61回システム制御情報学会研究発表講演会 (SCI2017), Invited, システム制御情報学会, 京都テルサ，京都, Domestic conference
24 May 2017

Towards Guideline for Applying Machine Learning into Care Support Systems,
Takadama, K
Oral presentation, English, The AAAI 2017 Spring Symposia, Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing, AAAI (The Association for the Advancement of Artificial Intelligence), The AAAI 2017 Spring Symposia, Stanford, USA, International conference
29 Mar. 2017

Improving Accuracy of Real-time Sleep Stage Estimation by Considering Personal Sleep Feature and Rapid Change of Sleep Behavior,
Harada, T; Kawashima, T; Morishima, M; Takadama, K
Oral presentation, English, The AAAI 2017 Spring Symposia, Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing, AAAI (The Association for the Advancement of Artificial Intelligence), The AAAI 2017 Spring Symposia, Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing, AAAI (The Association for the Advancement of Artificial Intelligence), Stanford, USA, International conference
29 Mar. 2017

Sleep Stage Estimation using heartrate approximate minimum method,
Tajima, Y; Harada, T; Takadama, K
Oral presentation, English, The AAAI 2017 Spring Symposia, Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing, AAAI (The Association for the Advancement of Artificial Intelligence), The AAAI 2017 Spring Symposia, Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing, AAAI (The Association for the Advancement of Artificial Intelligence), International conference
29 Mar. 2017

Visual Impression generation system based on Boids algorithm
Ishii, M; Kwon, J; Takadama, K; Sakamoto, M
Oral presentation, English, The AAAI 2017 Spring Symposia, Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing, AAAI (The Association for the Advancement of Artificial Intelligence), The AAAI 2017 Spring Symposia, Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing, AAAI (The Association for the Advancement of Artificial Intelligence), Stanford, USA, Peer-reviewed, International conference
28 Mar. 2017

Wellbeing AI Invited Speaker Abstracts,
Kido, T; Takadama, K
Oral presentation, English, The AAAI 2017 Spring Symposia, Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing, AAAI (The Association for the Advancement of Artificial Intelligence, The AAAI, Stanford, USA, International conference
27 Mar. 2017

Affinity Based Search Amount Control in Decomposition Based Evolutionary Multi-Objective Optimization,
Sato, H; Miyakawa, M; Takadama,K
Oral presentation, English, 10th EAI International Conference on Bio-inspired Information and Communications Technologies (BICT 2017), 10th EAI International Conference on Bio-inspired Information and Communications Technologies (BICT 2017), New Jersey, USA, International conference
15 Mar. 2017

不活性セルのシナプス更新による大脳新皮質アルゴリズムの予測精度向上に関する一検討
青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，第44回知能システムシンポジウム, 計測自動制御学会, 東京, Domestic conference
14 Mar. 2017

難易度と技術偏差に基づく学習目標生成を促すインタラクティブ学習支援
福田 千賀; 村田 暁紀; 石井 晴之; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第44回知能システムシンポジウム, 計測自動制御学会, 東京, Domestic conference
14 Mar. 2017

覚醒と浅睡眠に着目した圧力センサに基づく非侵襲的睡眠段階推定とその精度向上
上原 知里; 松本 和馬; 田島 友祐; 小峯 嵩裕; 原田 智広; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第44回知能システムシンポジウム, 計測自動制御学会, 東京, Domestic conference
14 Mar. 2017

最適性と多様性のトレードオフを考慮したノベルティサーチに基づく多目的進化計算
村田 暁紀; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 情報処理学会，第112回数理モデル化と問題解決研究発表会, 情報処理学会, 岩手, Domestic conference
27 Feb. 2017

Designing the learning goal space toward acquiring a creative learning skill
Okudo, T; Yamaguchi, T; Takadama, K
Oral presentation, English, The 22nd International Symposium on Artificial Life and Robotics (AROB'17), The 22nd International Symposium on Artificial Life and Robotics (AROB'17), Beppu, Oita, International conference
21 Jan. 2017

小型月着陸実証機SLIMの開発状況,
坂井 真一郎; 櫛木 賢一; 澤井 秀次郎; 福田 盛介; 佐藤 英一; 上野 誠也; 鎌田 弘之; 北薗 幸一; 小島 広久; 高玉 圭樹; 能見 公博; 樋口 丈浩; SLIM WG
Oral presentation, Japanese, 第17回 宇宙科学シンポジウム (SSS 2017), 相模原，神奈川
15 Jan. 2017

Automatic adjustment of selection pressure based on range of reward in learning classifier system.
Takato Tatsumi; Hiroyuki Sato; Keiki Takadama
Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, Berlin, Germany, July 15-19, 2017, ACM, http://doi.acm.org/10.1145/3071178.3080531
2017
2017 2017

Learning Classifier System Based on Variance of Reward for Clustering in Limited Number of Data: Application to Aged Care Plan
CHO ZAIRITU; TATSUMI TAKATO; TAKADAMA KEIKI
Japanese, Proceedings of the Japan Joint Automatic Control Conference, 自動制御連合講演会, http://ci.nii.ac.jp/naid/130006251472
2017
2017 2017

最適な進化計算パラメータの差異に基づく多目的最適化問題群のマッピングに関する検討
角口 元章; 宮川 みなみ; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第10回進化計算シンポジウム 2016, 進化計算学会, 千葉, Domestic conference
11 Dec. 2016

MOEA/D における解と重みベクトルの親和性に基づく探索量制御に関する一検討,
佐藤 寛之; 宮川 みなみ; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第10回進化計算シンポジウム 2016, 進化計算学会, Domestic conference
11 Dec. 2016

カッコウ探索に基づく複数のダイナミズムを含む動的環境への適応
梅内 祐太; 上野 史; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第10回進化計算シンポジウム 2016, 進化計算学会, 千葉, Domestic conference
10 Dec. 2016

学習分類子システムにおける評価回数に基づく分類子の選択圧自動調整
辰巳 嵩豊; Kovacs Tim; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第10回進化計算シンポジウム 2016, 進化計算学会, 千葉, Domestic conference
10 Dec. 2016

Random Forests を活用したLCS によるルールの一般化
土橋 功治; 松本 和馬; 辰巳 嵩豊; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第10回進化計算シンポジウム 2016, 進化計算学会, 千葉, Domestic conference
10 Dec. 2016

サブゴールの振り返りによる学習者の継続的学習支援
玉井 雄貴; 山口 智浩; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2016 (SSI2016), 計測自動制御学会, 滋賀 大津, Domestic conference
07 Dec. 2016

航空機到着機スケジューリングにおける最適性と多様性のトレードオフを考慮した進化計算
村田 暁紀; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2016 (SSI2016), 計測自動制御学会), 滋賀 大津, Domestic conference
07 Dec. 2016

可変長遺伝子型進化計算に基づく二輪ローバー型惑星探査機のスタック脱出行動最適化,
上野 史; 村田 暁紀; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2016 (SSI2016), 計測自動制御学会, 滋賀 大津, Domestic conference
07 Dec. 2016

MOEA/Dにおける連鎖更新法に基づく解集団初期化に関する検討
佐藤 寛之; 宮川 みなみ; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2016 (SSI2016), 計測自動制御学会, 滋賀 大津, Domestic conference
07 Dec. 2016

制約付き多目的最適化のための指向性交配における交叉量操作,
宮川 みなみ; 佐藤 裕二; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2016 (SSI2016), 計測自動制御学会, 滋賀 大津, Domestic conference
07 Dec. 2016

可変長遺伝子型進化計算に基づく二輪ローバー型惑星探査機のスタック脱出行動最適化
上野史; 村田暁紀; 高玉圭樹
Japanese, 計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM), http://jglobal.jst.go.jp/public/201702256069164760
06 Dec. 2016
06 Dec. 2016- 06 Dec. 2016

Crater Detection Method using Principle Component Analysis and its Evaluation
Takino, T; Nomura, I; Irie, J; Nagata, S; Kamata, H; Takadama, K; Fukuda, S; Sawai, S; Sakai, S
Post- proceeding of the 30th International Symposium on Space Technology and Science: ISTS2015
Nov. 2016

個人の睡眠特徴を考慮したリアルタイム睡眠深度推定の精度向上
原田 智広; 川嶋 隆宏; 森島 守人; 高玉 圭樹
Oral presentation, Japanese, 第3回 ヘルスケア・医療情報通信技術研究会 (MICT)，電子情報通信学会, Invited, 電子情報通信学会, 東京, Domestic conference
26 Sep. 2016

Effects of Chain-Reaction Initial Solution Arrangement in Decomposition-Based MOEAs
Sato,H; Miyakawa,M; Takadama,K
Oral presentation, English, The 9th Eurosim Congress on Modelling and Simulation, The 9th Eurosim Congress on Modelling and Simulation, Oulu,Finland, International conference
13 Sep. 2016

小型月着陸実証機「SLIM」プロジェクトの概要 JSASS-2016-4372
坂井 真一郎; 澤井 秀次郎; 福田 盛介; 櫛木 賢一; 佐藤 英一; 上野 誠也; 鎌田 弘之; 北薗 幸一; 小島 広久; 高玉 圭樹; 能見 公博; 樋口 丈浩
Oral presentation, Japanese, 日本航空宇宙学会，第60回宇宙科学技術連合講演会, 日本航空宇宙学会，第60回宇宙科学技術連合講演会, 函館, Domestic conference
08 Sep. 2016

主成分分析によるクレータ検出、クレータサイズ計測に関する研究 JSASS-2016-4375
鎌田 弘之; 中浜 優佳; 岡田 怜史; 森部 美沙子; 狩谷 和季; 石田 貴行; 福田 盛介; 高玉 圭樹; 坂井 真一郎; 澤井 秀次郎
Oral presentation, Japanese, 日本航空宇宙学会，第60回宇宙科学技術連合講演会, 日本航空宇宙学会，第60回宇宙科学技術連合講演会, 函館, Domestic conference
08 Sep. 2016

SLIM探査機における高度差や回転に対してロバストな自己位置推定アルゴリズム JSASS-2016-4376
石井 晴之; 村田 暁紀; 高玉 圭樹; 鎌田 弘之; 石田 貴行; 福田 盛介; 澤井 秀次郎; 坂井 真一郎
Oral presentation, Japanese, 日本航空宇宙学会，第60回宇宙科学技術連合講演会, 日本航空宇宙学会，第60回宇宙科学技術連合講演会, 函館, Domestic conference
08 Sep. 2016

クレータを用いた画像航法アルゴリズムの統合評価 JSASS-2016-4377
石田 貴行; 福田 盛介; 鎌田 弘之; 高玉 圭樹; 狩谷 和季; 坂井 真一郎; 澤井 秀次郎
Oral presentation, Japanese, 日本航空宇宙学会，第60回宇宙科学技術連合講演会, 日本航空宇宙学会，第60回宇宙科学技術連合講演会, 函館, Domestic conference
08 Sep. 2016

`Evolutionary Algorithmic Parameter Optimization of MOEAs for Multiple Multi-Objective Problems,
Kakuguchi, M; Miyakawa, M; Takadama, K; and; Sato, H
Oral presentation, English, 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2016), 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2016), Sapporo, International conference
26 Aug. 2016

Extracting Different Abstracted Level Rule with Variance-Based LCS,
Zhang, C; Tatsumi, T; Nakata, M; Takadama, K; Sato, H; Kovacs, T
Oral presentation, English, 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2016), 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2016), Sapporo, International conference
26 Aug. 2016

Learning Classifier System with Deep Autoencode
Matsumoto, K; Saito, R; Tajima, Y; Nakata, M; Sato, H; Kovacs T; Takadama, K
Oral presentation, English, IEEE World Congress on Computational Intelligence (WCCI 2016), IEEE, Vancouver, Canada, International conference
29 Jul. 2016

XCS-DH: Minimal Default Hierarchies in XCS
Kovacs, T; Rawles, S; Bull, L; Nakata, M; Takadama, K
Oral presentation, English, IEEE World Congress on Computational Intelligence (WCCI 2016), IEEE, Vancouver, Canada, International conference
29 Jul. 2016

Enhanced Decomposition-Based Many-Objective Optimization Using Supplemental Weight Vectors
Sato, H; Nakagawa, S; Miyakwa, M; Takadama, K
Oral presentation, English, IEEE World Congress on Computational Intelligence (WCCI 2016), IEEE, Vancouver, Canada, International conference
26 Jul. 2016

A Modified Cuckoo Search Algorithm for Dynamic Optimization Problem,
Umenai, Y; Uwano, F; Tajima, Y; Nakata, M; Sato, H; Takadama, K
Oral presentation, English, IEEE World Congress on Computational Intelligence (WCCI 2016), IEEE, Vancouver, Canada, International conference
26 Jul. 2016

Variance-based Learning Classifier System without Convergence of Reward Estimation
Tatsumi, T; Komine, T; Nakata, M; Sato, H; Kovacs, T; Takadama, K
Oral presentation, English, Genetic and Evolutionary Computation Conference (GECCO 2016), Genetic and Evolutionary Computation Conference (GECCO 2016), Denver, USA, International conference
22 Jul. 2016

Crater Detection Method using Principle Component Analysis and Its Evaluation,
Takino, T; Nomura, I; Irie, J; Nagata, S; Kamata, H; Takadama, K; Fukuda, S; Sawai, S; Sakai, S
Oral presentation, English, The 30th International Symposium on Space Technology and Science: ISTS2015, ISTS, Kobe, International conference
10 Jul. 2016

生体リズムに連動した音の特徴と睡眠因子との関連について
山木 清志; 森島 守人; 植屋 夕輝; 高玉 圭樹; 角谷 寛
Oral presentation, Japanese, 第41回日本睡眠学会シンポジウム，日本睡眠学会, 日本睡眠学会, 新宿・東京, Domestic conference
08 Jul. 2016

Biofeedback; 快眠音による睡眠介入への展開
森島 守人; 山木 清志; 川嶋 隆宏; 菅野 智; 原田 智広; 高玉 圭樹
Oral presentation, Japanese, 第41回日本睡眠学会シンポジウム，日本睡眠学会，, 日本睡眠学会, 新宿・東京, Domestic conference
08 Jul. 2016

Adaptive Learning Based on Genetic Algorithm for The Rover in Planetary Exploration,
Uwano, F; Murata, A; Takadama, K
Oral presentation, English, The 13th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS2016), Beijing, China, International conference
22 Jun. 2016

Possibility of Education Project based on Cansat,
Saito, R; Murata, A; Takadama, K
Oral presentation, English, The 13th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS2016), Beijing, China, International conference
22 Jun. 2016

Robust self-position estimation algorithm against displacement of crater detection in the SLIM spacecraft
Ishii, H; Usui, K; Takadama, K; Kamata, H; Fukuda S; Sawai,S; Sakai, S
Oral presentation, English, The 13th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS2016), Beijing, China, International conference
21 Jun. 2016

Robust self-position estimation algorithm against displacement of crater detection in the SLIM spacecraft,
Ishii, H; Usui, K; Takadama, K; Kamata, H; Fukuda S; Sawai,S; Sakai, S
Oral presentation, English, The 13th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS2016), International conference
19 Jun. 2016

Generation of Public Transportation Network for Commuter Stranded Problem,
Majima, T; Takadama, K; Watanabe, D; Katsuhara, M
Oral presentation, English, The 6th International Workshop on Emergent Intelligence on Networked Agents(WEIN'16), at 15h International Joint Conference on Autonomous Agents and Multi-agent Systems (AAMAS2016), Singapore, International conference
10 May 2016

Generation of Public Transportation Network for Commuter Stranded Problem
Majima, T; Takadama, K; Watanabe, D; Katsuhara, M
Oral presentation, English, The 6th International Workshop on Emergent Intelligence on Networked Agents (WEIN'16), at 15h International Joint Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2016), Singapore, International conference
10 May 2016

Effects on Sleep by “Cradle Sound” Adjusted to Heartbeat and Respiration
Morishima, M; Sugino, Y; Ueya, Y; Kadotani, H; Takadama, K
Oral presentation, English, The AAAI 2016 Spring Symposia, AAAI(The Association for the Advancement of Artificial Intelligence), Stanford, USA, International conference
22 Mar. 2016

Real-time Sleep Stage Estimation from Biological Data with Trigonometric Function Regression Model
Harada, T; Uwano, F; Komine, T; Tajima, Y; Kawashima, T; Morishima, M; Takadama, K
Oral presentation, English, The AAAI 2016 Spring Symposia, AAAI(The Association for the Advancement of Artificial Intelligence), Stanford, USA, International conference
22 Mar. 2016

Human Body Vibration Analysis - Toward the Next Generation Sleep Monitoring/Evaluation
Komine, T; Takadama, K; Nishino, S
Oral presentation, English, The AAAI 2016 Spring Symposia, AAAI(The Association for the Advancement of Artificial Intelligence), Stanford, USA, International conference
22 Mar. 2016

Well-being Computing Towards Health and Happiness Improvement: From Sleep Perspective,
Takadama, K
Oral presentation, English, The AAAI 2016 Spring Symposia, The Association for the Advancement of Artificial Intelligence, Stanford USA, International conference
21 Mar. 2016

半教師あり学習に基づく進化的クラスタリングによる快眠音の個別適応化
星野 秀彰; 村田 暁紀; 建部 尚紀; 中田 雅也; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第43回知能システムシンポジウム, 計測自動制御学会, 室蘭, Domestic conference
11 Mar. 2016

報酬値の分散に基づく学習分類子システムによる一般化度合の異なるルールの同時獲得
張 財立; 辰巳 嵩豊; 中田 雅也; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第43回知能システムシンポジウム, 計測自動制御学会，, 室蘭, Domestic conference
11 Mar. 2016

A Study on Directional Repair of Infeasible Solutions for Multi-Objective Knapsack Problems
Miyakawa, M; Takadama, K; Sato, H
Oral presentation, English, 2016 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2016), RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing, Hawaii, USA, International conference
07 Mar. 2016

A Study on Evolutionary Multi-level Robust Solution Search for Multi-objective Optimization Involving Multi-dimensional Noise
Hashimoto, T; Miyakawa, M; Takadama, K; Sato, H
Oral presentation, English, 2016 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2016), RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing, Hawaii, USA, International conference
07 Mar. 2016

無拘束センサから取得した日常生活データに基づく認知症判定：パーソナルビッグデータを用いた診療の可能性
高玉 圭樹; 外村 真悟; 青木 誠
Oral presentation, Japanese, 第12回日本病院総合診療医学会学術総会，日本病院総合診療医学会, 第12回日本病院総合診療医学会学術総会，日本病院総合診療医学会, 神奈川, Domestic conference
26 Feb. 2016

小型月着陸実証機SLIMについて
坂井 真一郎; 櫛木 賢一; 澤井 秀次郎; 福田 盛介; 佐藤 英一; 上野 誠也; 鎌田 弘之; 北薗 幸一; 高玉 圭樹; 能見 公博; 樋口 丈浩
Oral presentation, Japanese, 第16回 宇宙科学シンポジウム (SSS 2016), 第16回 宇宙科学シンポジウム (SSS 2016), 神奈川, Domestic conference
06 Jan. 2016

SLIM画像航法の開発進捗報告 ～アルゴリズムとそのハードウェア実装～
石田 貴行; 福田 盛介; 鎌田 弘之; 高玉 圭樹; 狩谷 和季; 野村 出; 滝野; 達也; 森部 美沙子; 臼居 浩太郎; 石井 晴之; 坂井 真一郎; 澤井秀 次郎
Oral presentation, Japanese, 第16回 宇宙科学シンポジウム (SSS 2016), 第16回 宇宙科学シンポジウム (SSS 2016), 神奈川, Domestic conference
06 Jan. 2016

補助重みベクトル群による多数目的最適化の促進に関する一検討
中川 智; 宮川 みなみ; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第9回進化計算シンポジウム 2015, 進化計算学会，第9回進化計算シンポジウム 2015, 愛知, Domestic conference
20 Dec. 2015

多次元ノイズを含む多目的最適化におけるスカラー化関数に基づくロバスト解探索
橋本 知尚; 宮川 みなみ; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第9回進化計算シンポジウム 2015, 進化計算学会，第9回進化計算シンポジウム 2015, 愛知, Domestic conference
20 Dec. 2015

多峰性関数における局所探索に基づく Cuckoo Search Algorithm
梅内祐太; 上野史; 中田雅也; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第9回進化計算シンポジウム 2015, 進化計算学会，第9回進化計算シンポジウム 2015, 愛知, Domestic conference
20 Dec. 2015

制約付き多目的最適化のための指向性交配における解の選出領域の適応制御に関する一検討
宮川 みなみ; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第9回進化計算シンポジウム 2015, 進化計算学会，第9回進化計算シンポジウム 2015, 愛知, Domestic conference
20 Dec. 2015

報酬分散の収束に依らない分散に基づく学習分類子システム
辰巳 嵩豊; 小峯 嵩裕; 中田雅也; Tim Kovacs; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第9回進化計算シンポジウム 2015, 進化計算学会，第9回進化計算シンポジウム 2015, 愛知, Domestic conference
20 Dec. 2015

RSSI に基づくSWARM 型ノード群によるモバイルアドホックネットワークの自律構築システム
建部 尚紀; 服部 聖彦; 加川 敏規; 高玉 圭樹; 大和田 泰伯; 浜口 清
Oral presentation, Japanese, 計測自動制御学会，第16回計測自動制御学会システムインテグレーション部門講演会, 計測自動制御学会, 名古屋, Domestic conference
14 Dec. 2015

Control of Variable Exchange Probability for Directed Mating in Evolutionary Constrained Multi-Objective Continuous Optimization
Miyakawa, M; Takadama, K; Sato H
Oral presentation, English, The 3rd International Symposium on Computational and Business Intelligence (ISCBI 2015), The 3rd International Symposium on Computational and Business Intelligence (ISCBI 2015), Bali Indonesia, International conference
08 Dec. 2015

Optimization of Aircraft Landing Route and Order: An approach of Hierarchical Evolutionary Computation
Murata, A; Nakata, M; Sato, H; Kovacs, T; Takadama, K
Oral presentation, English, The 9th International Conference on Bio-inspired Information and Communications Technologies (BICT2015), The 9th International Conference on Bio-inspired Information and Communications Technologies (BICT2015), New York USA, International conference
05 Dec. 2015

Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach
Uwano, F; Tatebe, N; Nakata, M; Takadama, K; Kovacs, T
Oral presentation, English, The 9th International Conference on Bio-inspired Information and Communications Technologies (BICT2015), The 9th International Conference on Bio-inspired Information and Communications Technologies (BICT2015), New York USA, International conference
03 Dec. 2015

Wellbeing Technology: 睡眠から健康と幸せを提供するエージェント
高玉 圭樹
Invited oral presentation, Japanese, 第101回研究開発セミナー，ヘルスケア・イノベーション (pp.51-65), 東京, Domestic conference
30 Nov. 2015

学習戦略を考慮した学習分類子システムの設計法の必要性
中田 雅也; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2015 (SSI2015), 計測自動制御学会, 函館, Domestic conference
19 Nov. 2015

振り返りサブタスクを用いた学習者の継続的学習支援
玉井 雄貴; 山口智浩; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2015 (SSI2015), 計測自動制御学会, 函館, Domestic conference
19 Nov. 2015

強化学習環境の規模拡大に対する知識の特殊化による再利用
臼居 浩太郎; 中田 雅也; Tim Kovacs; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2015 (SSI2015), 計測自動制御学会, 函館, Domestic conference
19 Nov. 2015

Multi-agent based Bus Route Optimization with Passenger Overflow Cascades Tolerance in Disaster Situations
Morimoto, S; Takadama, K; Majima, T; Watanabe, D; Katuhara, M
Oral presentation, English, The First International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM), The First International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM), Kyoto, International conference
28 Oct. 2015

Deployment of Wireless Mesh Network Using RSSI-Based Swarm Robots
Tatebe, N; Hattori; K. Kagawa, T; Owada, Y; Hamaguchi, K; Takadama, K
Oral presentation, English, The First International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM), Kyoto, International conference
28 Oct. 2015

Generating Hub-Spoke Network for Public Transportation
Majima, T; Takadama, K; Watanabe, D; Katsuhara, M
Oral presentation, English, The First International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM), kyoto, International conference
28 Oct. 2015

小型・高精度月着陸実証ミッション
坂井 真一郎; 澤井 秀次郎; 福田 盛介; 佐藤 英一; 鎌田 弘之; 北薗 幸一; 高玉 圭樹; 能見 公博; 樋口 丈浩
Oral presentation, Japanese, 日本航空宇宙学会，第59回宇宙科学技術連合講演会, 日本航空宇宙学会，第59回宇宙科学技術連合講演会, 鹿児島, Domestic conference
08 Oct. 2015

SLIM探査機におけるクレータ検出と自己位置推定アルゴリズムの統合評価およびその改善
石井 晴之; 臼居 浩太郎; 高玉 圭樹; 鎌田 弘之; 福田 盛介; 澤井 秀次郎; 坂井 真一郎
Oral presentation, Japanese, 日本航空宇宙学会，第59回宇宙科学技術連合講演会, 日本航空宇宙学会，第59回宇宙科学技術連合講演会, 鹿児島, Domestic conference
08 Oct. 2015

高精度クレータ検出のための主成分分析改善に関する研究
野村 出; 滝野 達也; 森部 美沙子; 鎌田 弘之; 高玉 圭樹; 福田 盛介; 澤井 秀次郎; 坂井 真一郎
Oral presentation, Japanese, 日本航空宇宙学会，第59回宇宙科学技術連合講演会, 日本航空宇宙学会，第59回宇宙科学技術連合講演会, 鹿児島, Domestic conference
08 Oct. 2015

パーソナルビッグデータからのライフスタイル設計：集合知から究極の個別知へ
高玉 圭樹
Invited oral presentation, Japanese, 2015年度ソサエティ大会，電子情報通信学会 pp.425-426, Invited, 電子情報通信学会, 仙台, Domestic conference
09 Sep. 2015

パーソナルビッグデータからのライフスタイル設計
高玉 圭樹
Invited oral presentation, Japanese, 2015年度ソサエティ大会，電子情報通信学会, ソサエティ大会，電子情報通信学会, 仙台, Domestic conference
09 Sep. 2015

学習分類子システムにおけるWilsonの一般化仮説への挑戦
中田 雅也; Will Browne; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第9回進化計算学会研究会, Domestic conference
07 Sep. 2015

Network Construction for Correct Opinion Sharing by Selecting a Curator Agent,
Saito, R; Tatebe, N; Takano R; Takadama, K
Oral presentation, English, The 34th Chinese Control Conference and SICE Annual Conference 2015 (CCC&SICE2015), SICE, 杭州，中国, International conference
29 Jul. 2015

Control of Crossed Genes Ratio for Directed Mating in Evolutionary Constrained Multi-Objective Optimization
Miyakwa, M; Takadama, K; Sato, H
Oral presentation, English, International Student Workshop on Genetic and Evolutionary Computation Conference (GECCO 2015),, International Student Workshop on Genetic and Evolutionary Computation Conference (GECCO 2015),, Madrid,Spain, International conference
11 Jul. 2015

A Potential of Evolutionary Rule-based Machine Learning for Real World Applications,
Takadama, K
Oral presentation, English, International Workshop on Evolutionary Rule-based Machine Learning (ERML) in Genetic and Evolutionary Computation Conference (GECCO 2015), International Workshop on Evolutionary Rule-based Machine Learning (ERML) in Genetic and Evolutionary Computation Conference (GECCO 2015), Madrid, Spain, International conference
11 Jul. 2015

Adjusting SLIM Spacecraft Location Estimation to Crater Detection for High Precision and Computational Time Reduction,
Usui, K; Harada, T; Takadama, K; Kamata, H; Fukuda S; Sawai, S; Sakai, S
Oral presentation, English, Adjusting SLIM Spacecraft Location Estimation to Crater Detection for High Precision and Computational Time Reduction,, KOBE, International conference
04 Jul. 2015

生体リズムに連動した音と音色の違いによる睡眠に及ぼす影響'
山木 清志; 植屋 夕輝; 石原 淳; 森島 守人; 原田 智広; 高玉 圭樹; 角谷 寛
Oral presentation, Japanese, 第40回日本睡眠学会シンポジウム，日本睡眠学会, 第40回日本睡眠学会シンポジウム，日本睡眠学会, 宇都宮, Domestic conference
03 Jul. 2015

パーソナルビッグデータで睡眠を改善する: コンシェルジュサービス介護支援エージェントとその展望
高玉 圭樹
Oral presentation, Japanese, ITヘルスケア学会学術大会，ITヘルスケア学会, Invited, ITヘルスケア学会学術大会，ITヘルスケア学会, 熊本, Domestic conference
07 Jun. 2015

Sightseeing Plan Recommendation System using Sequential Pattern Mining based on Adjacent Activities
Fujitsuka, T; Harada, T; Takadama, K; Sato, H; Yamaguchi, T
Oral presentation, English, The 10th Asian Control Conference 2015 (ASCC 2015, The 10th Asian Control Conference 2015 (ASCC 2015, Malaysia, International conference
03 Jun. 2015

Analyzing human's continuous learning processes with the reflection subtask,
Yamaguchi, T; Takemori, K; Tamai, Y; Takadama, K
Oral presentation, English, The 10th Asian Control Conference 2015 (ASCC 2015), The 10th Asian Control Conference 2015 (ASCC 2015), Malaysia, International conference
03 Jun. 2015

Extracting Both Generalized and Specialized Knowledge by XCS using Attribute Tracking and Feedback
Takadama, K; Nakata, M
Oral presentation, English, 2015 IEEE Congress on Evolutionary Computation (CEC2015), 2015 IEEE Congress on Evolutionary Computation (CEC2015), Sendai, International conference
28 May 2015

Handling Different Level of Unstable Reward Environment Through an Estimation of Reward Distribution in XCS,
Tatsumi, T; Komine, T; Sato, H; Takadama, K
Oral presentation, English, 2015 IEEE Congress on Evolutionary Computation (CEC2015), 2015 IEEE Congress on Evolutionary Computation (CEC2015), Sendai, International conference
28 May 2015

Detecting Shoplifting From Customer Behavior Data by Extended XCS-SL: Towards Feature Extraction on Class-Imbalanced Sequence Data
Sato, M; Usui, K; Nakata, M; Takadama, K
Oral presentation, English, 2015 IEEE Congress on Evolutionary Computation (CEC2015), 2015 IEEE Congress on Evolutionary Computation (CEC2015), International conference
28 May 2015

How Should Learning Classifier Systems Cover A State-Action Space?
Nakata, M; Lanzi, P.L; Kovacs, T; Browne, W.N; Takadama, K
Oral presentation, English, 2015 IEEE Congress on Evolutionary Computation (CEC2015), 2015 IEEE Congress on Evolutionary Computation (CEC2015), Sendai, International conference
28 May 2015

Directed Mating Using Inverted PBI Function for Constrained Multi-Objective Optimization,
Miyakwa, M; Takadama, K; Sato, H
Oral presentation, English, 2015 IEEE Congress on Evolutionary Computation (CEC2015), 2015 IEEE Congress on Evolutionary Computation (CEC2015), Sendai, International conference
28 May 2015

Ship Route Evolutionary Optimization of Multiple Ship Companies for Distributed Coordination of Resources
Takadama, K; Azuma, E; Sato, H; Majima, T; Watanabe, D; Katuhara, M
Oral presentation, English, 2015 IEEE Congress on Evolutionary Computation (CEC2015), 2015 IEEE Congress on Evolutionary Computation (CEC2015), Sendai, International conference
27 May 2015

Toward Robustness Against Environmental Change Speed by Artificial Bee Colony Algorithm based on Local Information Sharing
Takano, R; Harada, T; Sato, H; Takadama, K
Oral presentation, English, 2015 IEEE Congress on Evolutionary Computation (CEC2015), 2015 IEEE Congress on Evolutionary Computation (CEC2015), Sendai, International conference
27 May 2015

Estimating Surrounding Symptom Level of Dementia Person by Sleep Stage,
Tomura, S; Harada, T; Sato; H. Takadama, K; Aoki. M
Oral presentation, English, The Ninth International Symposium on Medical Information and Communication Technology (ISMICT 2015), Kanagawa, International conference
26 Mar. 2015

Towards Ambient intelligence System for Good Sleep By Sound Adjusted to Heartbeat and Respiration
Takadama, K; Tajima, Y; Harada, T; Ishihara, A; Morishima, M
Oral presentation, English, The AAAI 2015 Spring Symposia}, Ambient Intelligence for Health and Cognitive Enhancement, AAAI (The Association for the Advancement of Artificial Intelligence), The AAAI 2015 Spring Symposia, Stanford, USA, International conference
24 Mar. 2015

Detecting Aged Person's Sliding Feet from Time Series Data of Foot Pressure
Komine, T; Takadama, K
Oral presentation, English, The AAAI 2015 Spring Symposia, Ambient Intelligence for Health and Cognitive Enhancement, AAAI (The Association for the Advancement of Artificial Intelligence), The AAAI 2015 Spring Symposia, Stanford, USA, International conference
24 Mar. 2015

ジレンマ問題におけるマルチエージェント間協調のための内部報酬推算
上野 史; 建部 尚紀; 中田 雅也; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第42回知能システムシンポジウム, 計測自動制御学会, 神戸, Domestic conference
18 Mar. 2015

階層型進化計算を用いた動的航空機着陸経路スケジューリング
村田 暁紀; 森本 紗矢香; 神馬 隆博; 原田 智広; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第42回知能システムシンポジウム，, 計測自動制御学会，, 神戸, Domestic conference
18 Mar. 2015

Analyzing human's continuous learning ability toward the intelligent robotics,
Yamaguchi, T; Takemori, K; Tamai, Y; Takadama, K
Oral presentation, English, The 20th International Symposium on Artificial Life and Robotics (AROB'15), Beppu, Oita, International conference
23 Jan. 2015

コンシェルジュサービス介護支援: 睡眠からのライフスタイル設計への展開
高玉 圭樹
Public discourse, Japanese, 共同研究カンファレンス, 共同研究カンファレンス, 川崎, Domestic conference
13 Jan. 2015

SLIM画像航法の研究開発状況 (その2:クレータマッチングアルゴリズム)
高玉 圭樹; 原田 智広; 臼居 浩太郎; 鎌田 弘之; 小沢 愼治; 福田 盛介; 澤井 秀次郎
Oral presentation, Japanese, 第14回 宇宙科学シンポジウム (SSS 2014)
Jan. 2015

制約付き多数目的最適化のためのリファレンスラインを用いた指向性交配の検討
宮川 みなみ; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第8回進化計算シンポジウム 2014, 進化計算学会, 広島, Domestic conference
20 Dec. 2014

不安定報酬環境下における学習分類子システム
辰巳 嵩豊; 小峯 嵩裕; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第8回進化計算シンポジウム 2014, 進化計算学会, 広島, Domestic conference
20 Dec. 2014

時変環境における局所的情報共有によるArtificial Bee Colonyアルゴリズム'
高野 諒; 原田 智広; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 情報処理学会，第102回数理モデル化と問題解決研究発表会, 奈良, Domestic conference
09 Dec. 2014

時系列行動を評価するパターンマイニングによる外出プラン推薦システム
藤塚 拓馬; 原田; 智広; 佐藤; 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2014, 計測自動制御学会，システム・情報部門 学術講演会 2014 (SSI2014), 岡山, Domestic conference
21 Nov. 2014

災害時におけるボトルネック解消に向けたバス路線網最適化
森本 紗矢香; 神馬 隆博; 北川 広登; 高玉 圭樹; 間島 隆博; 渡部 大輔; 勝原光次郎
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2014, 計測自動制御学会，システム・情報部門 学術講演会, 岡山, Domestic conference
21 Nov. 2014

ピンポイント着陸に向けたSLIM探査機の自己位置推定とその展開
臼居 浩太郎; 原田 智広; 高玉 圭樹; 鎌田 弘之; 福田 盛介; 澤井 秀次郎
Oral presentation, Japanese, 日本航空宇宙学会，第58回宇宙科学技術連合講演会, 日本航空宇宙学会，第58回宇宙科学技術連合講演会, 長崎, Domestic conference
13 Nov. 2014

主成分分析によるクレータ検出の特性評価と改善について
野村 出; 滝野 達也; 入江 順也; 永田 心; 鎌田 弘之; 高玉 圭樹; 福田 盛介
Oral presentation, Japanese, 日本航空宇宙学会，第58回宇宙科学技術連合講演会, Domestic conference
13 Nov. 2014

Artificial Bee Colony Algorithm based on Local Information Sharing in Dynamic Environment,
Takano,R; Harada,T; Sato,H; Takadama,K
Oral presentation, English, IES2014, IES, Singapore, International conference
12 Nov. 2014

Artificial Bee Colony Algorithm based on Local Information Sharing in Dynamic Environment
Takano, R; Harada, T; Sato, H; Takadama, K
Oral presentation, English, The 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems(IES2014), International conference
11 Nov. 2014

Multi-agent based Bus Route Optimization for Restricting Passenger Traffic Bottlenecks in Disaster Situations
Morimoto, S; Jimba, T; Kitagawa, H; Takadama, K; Majima, T; Watanabe, D; Katuhara, M
Oral presentation, English, The 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems(IES2014), International conference
11 Nov. 2014

Multi Objective Optimization for Route Planning and FleetAssignment in Regular and Non-regular Flights
Jimba, T; Harada, T; Sato, H; Takadama, K
Oral presentation, English, The 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES2014), International conference
10 Nov. 2014

創発現象を工学に結びつけることはできるか? ― マルチエージェントシステムからの可能性
高玉 圭樹
Oral presentation, Japanese, システム制御情報学会特別企画，パネル討論「群れを理解し操り創る」， 第57回自動制御連合講演会, 計測自動制御学会, 群馬, Domestic conference
10 Nov. 2014

Multiagent-based ABC algorithm for Autonomous Rescue Agent Cooperation
Takano, R; Yamazaki, D; Ichikawa, Y; Hattori, K; Takadama, K
Oral presentation, English, The IEEE 2014 International Conference On Systems, Man and Cybernetics (SMC 2014), International conference
06 Oct. 2014

Reports of the 2014 AAAI Spring Symposium Series,
Jain, M; Jiang, A.X; Kido, T; Takadama, K; Mercer, E.G; Rungta, N; Waser, M; Wagner, A; Burke, J; Sofge, D; Lawless,W; Sridharan, M; Hawes, N; Hwang, T
Oral presentation, English, AI magazine,, International conference
29 Sep. 2014

Multi-UAV Path Planning of 3D Virtual Environment Based on Improved Particle Swarm Optimization
Li, D; Takadama, K
Oral presentation, English, The UEC International Mini-Conference for Exchange Students on Informatics & Engineering and Information Systems, Domestic conference
07 Aug. 2014

主成分分析を用いたクレータ検出法の特性評価に関する研究
野村 出; 滝野 達也; 永田 心; 入江 順也; 鎌田 弘之; 高玉 圭樹; 福田 盛介; 澤井 秀次郎; 坂井 真一郎
Oral presentation, Japanese, 主成分分析を用いたクレータ検出法の特性評価に関する研究， 第24回アストロダイナミクスシンポジウム
29 Jul. 2014

システム統合におけるシステム間の関係性とその妥当性検証: SLIM(Smart Lander for Investigating Moon)における自己位置推定システムを例として
高玉 圭樹; 原田 智広; 鎌田 弘之; 福田 盛介; 澤井 秀次郎
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門，第7回関係論的システム科学調査研究会
05 Jul. 2014

非同期評価に基づく遺伝的プログラミングによる機械語プログラムの進化
原田 智広; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門，第7回関係論的システム科学調査研究会
04 Jul. 2014

呼吸に連動した音が睡眠に及ぼす影響の予備的検討
石原 淳; 森島 守人; 原田 智広; 田島 友祐; 佐藤 圭二; 高玉 圭樹
Oral presentation, Japanese, 第39回日本睡眠学会シンポジウム，日本睡眠学会, 日本睡眠学会, 徳島, Domestic conference
03 Jul. 2014

ネットワークのトポロジーと規模にロバストな複数エージェント間情報共有
石井 将文; 高野 諒; 山崎 大地; 高玉 圭樹
Oral presentation, Japanese, 人工知能学会，第28回全国大会
12 Jun. 2014

invisibleな迷路タスクを用いた人の学習過程の可視化による継続的学習の支援
山口 智浩; 竹森 孝樹; 高玉 圭樹
Oral presentation, Japanese, 人工知能学会，第28回全国大会
12 Jun. 2014

パレート報酬を考慮した政策群アーカイブに基づくマルチエージェント強化学習
市川 嘉裕; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第41回知能システムシンポジウム
14 Mar. 2014

災害時におけるボトルネック解消に向けたバス路線網最適化
森本 紗矢香; 神馬 隆博; 北川 広登; 間島 隆博; 渡部 大輔; 勝原 光次郎; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第41回知能システムシンポジウム
14 Mar. 2014

Invisibleな迷路タスクを用いた人の学習過程の可視化
山口 智浩; 竹森 孝樹; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第41回知能システムシンポジウム
14 Mar. 2014

健康維持に向けた睡眠改善コンシェルジュ
高玉 圭樹
Invited oral presentation, Japanese, 第15回日本健康支援学会年次学術大会，日本健康支援学会
09 Mar. 2014

複素数を用いた学習分類子システムによる POMDPs 環境への展開
山崎 大地; 中田 雅也; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第6回進化計算学会研究会
07 Mar. 2014

動的環境における局所的情報共有による分散型 ABC アルゴリズム
高野 諒; 市川 嘉裕; 服部 聖彦; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第6回進化計算学会研究会
07 Mar. 2014

定期便と不定期便の同時獲得型多目的路線網最適化
神馬 隆博; 佐藤 圭二; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会，第6回進化計算学会研究会
06 Mar. 2014

第三の親個体とそのアーカイブを用いたリファレンス評価による非同期進化
原田 智広; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第6回進化計算学会研究会
06 Mar. 2014

動的環境おける頑健な確率モデルの学習：ナップサック問題から実問題まで
田島 友祐; 中田 雅也; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第6回進化計算学会研究会
06 Mar. 2014

Visualizing mental learning processes with invisible mazes for assisting continuous learning
山口 智浩; 竹森 孝樹; 高玉 圭樹
Japanese, 第28回人工知能学会全国大会論文集, 人工知能学会, http://id.ndl.go.jp/bib/025483578
Mar. 2014
Mar. 2014 Mar. 2014

関係性を作る: 内部欲求と外部状況の差に基づくエージェントの目的生成とその可能性
高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門，第7回関係論的システム科学調査研究会
26 Feb. 2014

リンケージ型学習分類子システムによる類似環境に適応可能な汎用的知識の獲得と活用法の提案および性能評価
臼居浩太郎; 中田雅也; 髙玉圭樹
Japanese, 研究報告数理モデル化と問題解決（MPS）, 一般社団法人情報処理学会, http://ci.nii.ac.jp/naid/110009673888, 本研究では，類似する環境に適応可能な遺伝的機械学習手法としてリンケージ型学習分類子システム (XCSAM with Linkage-Classifier: XCSL) を提案する．具体的には，行動最適性の高い分類子同士の結びつきを表現したリンケージ型分類子 (Linkage-Classifier) を形成し，行動選択に利用する．提案手法の有効性を検証するために，Multi-step 問題の一般的なベンチマーク問題である Block World 問題を用いて，従来手法 XCS, XCSAM との比較をしたところ，類似する環境に変化する場合において，リンケージ型分類子を活用することで XCS に対しては約 18%，XCSAM に対しては約 25%の学習回数で新しい環境に適応可能であることが明らかとなった．
24 Feb. 2014
24 Feb. 2014- 24 Feb. 2014

Application of Community Detection Method to Generating Public Transport Network
Majima, T; Takadama, K; Watanabe, D; Katsuhara, M
Oral presentation, English, The 8th International Conference on Bio-inspired Information and Communications Technologies (BICT2014), Boston, USA, International conference
12 Feb. 2014

小型月着陸技術実証機"SLIM"の提案概要
坂井 真一郎; 澤井 秀次郎; 福田 盛介; 佐藤 英一; 鎌田 弘之; 北薗 幸一; 高玉 圭樹; 能見 公博; 樋口 丈浩
Oral presentation, Japanese, 第15回 宇宙科学シンポジウム (SSS 2015), 宇宙航空研究開発機構宇宙科学研究所, 神奈川県 相模原市, Domestic conference
16 Jan. 2014

SLIM画像航法の研究開発状況 (その3:ハードウェア実装，その他)
福田 盛介; 鎌田 弘之; 高玉 圭樹; 本田 翔平; 入江 順也; 永田 心; 杉本 悠太; 原田 智広; 金澤 慧; 小沢 愼治; 中谷幸司; 坂井真一郎; 澤井秀次郎
Oral presentation, Japanese, 第14回 宇宙科学シンポジウム (SSS 2014)
09 Jan. 2014

SLIM画像航法の研究開発状況 (その1:クレータ抽出アルゴリズム)
永田 心; 入江 順也; 本田 翔平; 鎌田 弘之; 高玉 圭樹; 小沢 愼治; 中谷 幸司; 福田 盛介; 澤井 秀次郎
Oral presentation, Japanese, 第14回 宇宙科学シンポジウム (SSS 2014)
09 Jan. 2014

SLIMシステム概要
澤井 秀次郎; 坂井 真一郎; 福田 盛介; 佐藤 英一; 北薗 幸一; 河野 太郎; 佐伯 孝尚; 樋口 丈浩; 高玉 圭樹
Oral presentation, Japanese, 第15回 宇宙科学シンポジウム (SSS 2015), 宇宙航空研究開発機構宇宙科学研究所, 神奈川県 相模原市, Domestic conference
06 Jan. 2014

`SLIM画像航法の検討
福田 盛介; 鎌田 弘之; 高玉 圭樹; 野村 出; 滝野 達也; 入江 順也; 永田 心; 原田 智広; 臼居 浩太郎; 坂井 真一郎; 澤井 秀次郎
Oral presentation, Japanese, 第15回 宇宙科学シンポジウム (SSS 2015), JAXA, 神奈川県 相模原, Domestic conference
06 Jan. 2014

動的環境におけるアーカイブ型学習分類子システム
小峯 嵩裕; 佐藤 拳人; 田島 友祐; 中田 雅也; 高玉 圭樹
Oral presentation, Japanese, 第7回進化計算シンポジウム 2013，進化計算学会
15 Dec. 2013

ピボット型一般化に基づく交叉による一般化能力と解探索性能の向上
佐藤 圭二; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第7回進化計算シンポジウム 2013，進化計算学会
15 Dec. 2013

リファレンス評価による非同期進化：第三の親選択とそのアーカイブ
原田 智広; 高玉 圭樹
Oral presentation, Japanese, 第7回進化計算シンポジウム 2013，進化計算学会
15 Dec. 2013

指向性交配における有用な実行不可能解の選択領域制御に関する検討
宮川 みなみ; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第7回進化計算シンポジウム 2013，進化計算学会
14 Dec. 2013

環境変化にロバストなリンケージ型学習分類子システム
臼居 浩太郎; 中田 雅也; 高玉 圭樹
Oral presentation, Japanese, 第7回進化計算シンポジウム 2013，進化計算学会
14 Dec. 2013

災害時における道路寸断に対するバス路線網修正と最適化
北川 広登; 佐藤 圭二; 佐藤 寛之; 服部 聖彦; 高玉圭樹
Oral presentation, Japanese, 第7回進化計算シンポジウム 2013，進化計算学会
14 Dec. 2013

路線網利用者の経路選択傾向と路線網構築法
間島 隆博; 高玉 圭樹; 渡部 大輔; 勝原 光治郎
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2013 (SSI2013)
18 Nov. 2013

水上輸送を活用した災害時医療搬送シミュレーション
渡部 大輔; 間島 隆博; 高玉 圭樹; 勝原 光治郎
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2013 (SSI2013)
18 Nov. 2013

invisibleな刺激に基づく，人間の継続的学習の促進
竹森 孝樹; 山口 智浩; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2013 (SSI2013)
18 Nov. 2013

分解に基づく進化型多目的最適化法の効果的な並列化に関する検討
佐藤 寛之; 佐藤 圭二; 宮川 みなみ; Elizabeth Perez-Cortes; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2013 (SSI2013)
18 Nov. 2013

SLIMにおける実撮影画像のクレータ検出からの自己位置推定 － クレータ誤検出にロバストな自己位置推定アルゴリズムの評価
原田 智広; 杉本 悠太; 高玉 圭樹; 鎌田 弘之; 小沢 愼治; 福田 盛介; 澤井 秀次郎
Oral presentation, Japanese, 日本航空宇宙学会，第57回宇宙科学技術連合講演会
11 Oct. 2013

主成分分析を用いたクレータ検出の高精度化に関する研究
入江 順也; 永田 心; 本田 翔平; 鎌田 弘之; 金澤 慧; 高玉 圭樹; 小澤 愼治; 中谷 幸司; 福田 盛介; 澤井 秀次郎
Oral presentation, Japanese, 日本航空宇宙学会，第57回宇宙科学技術連合講演会
11 Oct. 2013

複素強化学習を用いた学習分類子システムによるPOMDPs環境への展開
山崎大地; 中田雅也; 高玉圭樹
Oral presentation, Japanese, 情報処理学会,第９５回数理モデル化と問題解決研究発表会
Sep. 2013

可変ピボット型一般化による多様性向上と高速化
佐藤圭二; 佐藤寛之; 高玉圭樹
Oral presentation, Japanese, 情報処理学会,第９５回数理モデル化と問題解決研究発表会
Sep. 2013

エージェントにおける知の設計：人間を超える知を目指して
高玉圭樹
Keynote oral presentation, Japanese, JAWS2013, 人工知能学会, Domestic conference
Sep. 2013