標題: | Strength Improvement and Analysis for an MCTS-Based Chinese Dark Chess Program |
作者: | Hsueh, Chu-Hsuan Wu, I-Chen Tseng, Wen-Jie Yen, Shi-Jim Chen, Jr-Chang 資訊工程學系 Department of Computer Science |
公開日期: | 2015 |
摘要: | 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 %. |
URI: | http://dx.doi.org/10.1007/978-3-319-27992-3_4 http://hdl.handle.net/11536/135624 |
ISBN: | 978-3-319-27992-3 978-3-319-27991-6 |
ISSN: | 0302-9743 |
DOI: | 10.1007/978-3-319-27992-3_4 |
期刊: | ADVANCES IN COMPUTER GAMES, ACG 2015 |
Volume: | 9525 |
起始頁: | 29 |
結束頁: | 40 |
顯示於類別: | 會議論文 |