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