2015年
Strength Improvement and Analysis for an MCTS-Based Chinese Dark Chess Program
Advances in Computer Games, ACG 2015
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- 巻
- 9525
- 号
- 開始ページ
- 29
- 終了ページ
- 40
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1007/978-3-319-27992-3_4
- 出版者・発行元
- SPRINGER INT PUBLISHING AG
Monte-Carlo tree search (MCTS) has been successfully applied to Chinese dark chess (CDC). In this paper, we study how to improve and analyze the playing strength of an MCTS-based CDC program, named DARKKNIGHT, which won the CDC tournament in the 17th Computer Olympiad. We incorporate the three recent techniques, early playout terminations, implicit minimax backups, and quality-based rewards, into the program. For early playout terminations, playouts end when reaching states with likely outcomes. Implicit minimax backups use heuristic evaluations to help guide selections of MCTS. Quality-based rewards adjust rewards based on online collected information. Our experiments showed that the win rates against the original DARKKNIGHT were 60.75 %, 70.90 % and 59.00 %, respectively for incorporating the three techniques. By incorporating all together, we obtained a win rate of 76.70 %.
- リンク情報
- ID情報
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- DOI : 10.1007/978-3-319-27992-3_4
- ISSN : 0302-9743
- Web of Science ID : WOS:000375768500004