Searching Optimal Combat Strategies of On-line Action Role-playing Game using Discrete Competitive Markov Decision Process

• Item type: Symposium(1)
• 公開日: 2011-11-04
• タイトル: Searching Optimal Combat Strategies of On-line Action Role-playing Game using Discrete Competitive Markov Decision Process
• タイトル / 言語: en
• タイトル: Searching Optimal Combat Strategies of On-line Action Role-playing Game using Discrete Competitive Markov Decision Process
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: Graduate School of Advanced Integration Science, Chiba University
• 著者所属: Graduate School of Advanced Integration Science, Chiba University
• 著者所属: Graduate School of Advanced Integration Science, Chiba University
• 著者所属(英): en
• 著者所属(英): Graduate School of Advanced Integration Science, Chiba University
• 著者所属(英): en
• 著者所属(英): Graduate School of Advanced Integration Science, Chiba University
• 著者所属(英): en
• 著者所属(英): Graduate School of Advanced Integration Science, Chiba University
• 著者名: Chen, Haoyang; Yasukuni, Mori; Ikuo, Matsuba
• 著者名(英): Chen, Haoyang; Yasukuni, Mori; Ikuo, Matsuba
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In the case of on-line Action Role-playing Game, the combat strategies can be divided into three distinct classes, Strategy of Motion(SM), Strategy of Attacking Occasion(SAO) and Strategy of Using Skill(SUS). In this paper, we analyze such strategies of a basic game model in which the combat is modeled by Discrete Competitive Markov Decision Process. By introducing the chase model and the combat assistant technology, we identify the optimal SM and the optimal SAO, successfully. Also, we propose an evolutionary framework, including integration with competitive and cooperative coevolution, to search the optimal SUS which is regarded as the Nash Equilibrium Point of the strategy space. Moreover, some experiments are made to demonstrate that the proposed framework has the ability to retrieve the optimal SUS. Furthermore, from the results, it is shown that using cooperative coevolution is much more efficient than using simple evolutionary algorithm.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In the case of on-line Action Role-playing Game, the combat strategies can be divided into three distinct classes, Strategy of Motion(SM), Strategy of Attacking Occasion(SAO) and Strategy of Using Skill(SUS). In this paper, we analyze such strategies of a basic game model in which the combat is modeled by Discrete Competitive Markov Decision Process. By introducing the chase model and the combat assistant technology, we identify the optimal SM and the optimal SAO, successfully. Also, we propose an evolutionary framework, including integration with competitive and cooperative coevolution, to search the optimal SUS which is regarded as the Nash Equilibrium Point of the strategy space. Moreover, some experiments are made to demonstrate that the proposed framework has the ability to retrieve the optimal SUS. Furthermore, from the results, it is shown that using cooperative coevolution is much more efficient than using simple evolutionary algorithm.
• 書誌情報: ゲームプログラミングワークショップ2011論文集; 巻 2011, 号 6, p. 120-127, 発行日 2011-10-28
• 出版者 / 言語: ja
• 出版者: 情報処理学会