Multiagent Decision Making on Transportation Networks

• Item type: JInfP(1)
• 公開日: 2014-04-15
• タイトル: Multiagent Decision Making on Transportation Networks
• タイトル / 言語: en
• タイトル: Multiagent Decision Making on Transportation Networks
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [Special Issue on Multiagent-based Societal Systems] game theory, adversarial reinforcement learning, Experience-Weighted Attraction, stochastic game
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: University of Minnesota, Dept. of Computer Science and Engineering
• 著者所属: University of Minnesota, Dept. of Computer Science and Engineering
• 著者所属: University of Minnesota, Dept. of Computer Science and Engineering
• 著者所属(英): en
• 著者所属(英): University of Minnesota, Dept. of Computer Science and Engineering
• 著者所属(英): en
• 著者所属(英): University of Minnesota, Dept. of Computer Science and Engineering
• 著者所属(英): en
• 著者所属(英): University of Minnesota, Dept. of Computer Science and Engineering
• 著者名: Ernesto, Nunes; Julio, Godoy; Maria, Gini
• 著者名(英): Ernesto, Nunes; Julio, Godoy; Maria, Gini
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: We model a transportation network where agents of different types operate with conflicting objectives: drivers want to drive at high speeds to reach their destinations faster, while police units want to prevent unlawful speeding. Police units have to efficiently allocate their limited resources to monitor roads and catch speeders, who try to avoid being caught. Assuming that police and drivers make strategic choices, the problem can be modeled using game theory. We describe the models and algorithms we developed and validate them on synthetic and real traffic data from different maps.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: We model a transportation network where agents of different types operate with conflicting objectives: drivers want to drive at high speeds to reach their destinations faster, while police units want to prevent unlawful speeding. Police units have to efficiently allocate their limited resources to monitor roads and catch speeders, who try to avoid being caught. Assuming that police and drivers make strategic choices, the problem can be modeled using game theory. We describe the models and algorithms we developed and validate them on synthetic and real traffic data from different maps.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA00700121
• 書誌情報: Journal of information processing; 巻 22, 号 2, p. 307-318, 発行日 2014-04-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6652
• 出版者 / 言語: ja
• 出版者: 情報処理学会