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予測能力を持つサッカーエージェントによる協調戦術の獲得
http://hdl.handle.net/2261/8105
http://hdl.handle.net/2261/81059e9d6d4e-f476-4dcd-8022-4d05334f60c1
名前 / ファイル | ライセンス | アクション |
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AI_16_01.pdf (2.8 MB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2007-12-27 | |||||
タイトル | ||||||
タイトル | 予測能力を持つサッカーエージェントによる協調戦術の獲得 | |||||
言語 | ||||||
言語 | jpn | |||||
キーワード | ||||||
主題 | soccer agents | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | cognitive modeling | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | cooperative tactics | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | Bayesian prediction | |||||
主題Scheme | Other | |||||
キーワード | ||||||
主題 | adaptive learning | |||||
主題Scheme | Other | |||||
資源タイプ | ||||||
資源 | http://purl.org/coar/resource_type/c_6501 | |||||
タイプ | journal article | |||||
その他のタイトル | ||||||
その他のタイトル | Acquisition of Cooperative Tactics by Soccer Agents with Ability of Prediction and Learning | |||||
著者 |
熊田, 陽一郎
× 熊田, 陽一郎× 植田, 一博 |
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著者別名 | ||||||
識別子 | 105948 | |||||
識別子Scheme | WEKO | |||||
姓名 | Kumada, Yoichiro | |||||
著者別名 | ||||||
識別子 | 105949 | |||||
識別子Scheme | WEKO | |||||
姓名 | Ueda, Kazuhiro | |||||
著者所属 | ||||||
著者所属 | 東京大学大学院総合文化研究科広域科学専攻 | |||||
著者所属 | ||||||
著者所属 | Department of Systems Science, The University of Tokyo | |||||
著者所属 | ||||||
著者所属 | 東京大学大学院情報学環・学際情報学府 | |||||
著者所属 | ||||||
著者所属 | Interfaculty Initiative in Information Studies, The University of Tokyo | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Designing soccer agents operating on the Soccer Server has became a standard problem in the multiagent domain, and this paper describes the soccer agents that can learn to make use of cooperative tactics. Considering the ways actual coaches of soccer enable their players learn to execute the soccer tactics, we developed a method of agents' learning to distinguish good tactics from not-so-good tactics. It is made up mainly of small practical tasks requiring a few agents, of acquisition of appropriate cognitive maps by decomposing the situations into grid information, and of optimization of total play by a kind of adaptive learning. Because the agents perceive the environment as a grid, they have a finite number of condition spaces and are able to predict the behavior of opponents by learning the conditonal probabilities. Each condition has its own utility learned in an evolutionary method. | |||||
書誌情報 |
人工知能学会論文誌 巻 16, 号 1, p. 120-127, 発行日 2001 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 13460714 | |||||
書誌レコードID | ||||||
収録物識別子タイプ | NCID | |||||
収録物識別子 | AA11579226 | |||||
DOI | ||||||
識別子タイプ | DOI | |||||
関連識別子 | info:doi/10.1527/tjsai.16.120 | |||||
フォーマット | ||||||
内容記述タイプ | Other | |||||
内容記述 | application/pdf | |||||
日本十進分類法 | ||||||
主題 | 007.13 | |||||
主題Scheme | NDC | |||||
出版者 | ||||||
出版者 | 人工知能学会 |