マルチエージェントシステムのモデル化のための協調的学習オートマトンチームモデル

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2017/05/01

タイトル(英語): A Collaborative Learning Automata Team Model for Modeling Multi-Agent Systems

著者名: 汪　岑（千葉大学），小圷　成一（千葉大学），岡本　卓（千葉大学），銭　飛（関東学院大学）

著者名(英語): Cen Wang (Chiba University), Seiichi Koakutsu (Chiba University), Takashi Okamoto (Chiba University), Fei Qian (Kanto Gakuin University)

キーワード: 学習オートマトン，チームモデル，強化学習，協調的行動，マルチエージェントシステム，最短経路探索　　learning automaton，team model，reinforcemenet learning，collaboratiive behavior，multi-agent system，shortest path routing

要約(英語): The learning automaton (LA) team model has been proposed as one method for modeling multi-agent systems. It is modeled as a non-cooperative game of learning automata. In this model, each LA operates independently from each other, and there exists a Nash equilibrium, i.e. the existance of an optimal mixed strategy in the mixed strategy space of the game has been proven. However, for modelling multi-agent systems more generally, the information exchange among agents and the acquisition of cooperative behaviors such as the formation of autonomous community are required. In this paper, in order to complement the LA team model, we propose a new LA team model with some fully or partially collaborative learning behaviors. In this new model, each automaton performs reinforcement learning process in order to identify random environments exchanging information with its adjacent automata. Several computer simulations indicate the availability of the proposed model.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.137 No.5 （2017）

本誌掲載ページ: 759-767 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/137/5/137_759/_article/-char/ja/