沈阳航空航天大学学报2025，Vol.42Issue(4)：59-67,9.DOI:10.3969/j.issn.2095-1248.2025.04.009
融合先验知识的藏久棋MCTS算法优化
Optimization of MCTS algorithm for Tibetan Jiu Chess by incorporating prior knowledge
摘要
Abstract
Tibetan Jiu Chess,a traditional folk chess game,is a complete information game that carries the profound Tibetan civilization and splendid culture.In view of the complexity of the rule system and the diversity of the game changes,the traditional game search algorithm is unable to cope with the vast game board and complex strategies.In order to improve the intelligence level of Tibetan Jiu Chess,a Monte Carlo tree search(MCTS)algorithm optimization strategy incorporating prior knowledge was proposed.The strategy was based on deep reinforcement learning in the key phases of layout planning and move strategy,and the strategy selection optimization function and evaluation function were designed by integrating the prior knowledge of domain experts.The search process of MCTS was efficiently guided by functions,and the best model for high-quality tessellation could be trained.Experimental results show that the improved MCTS algorithm achieves significant performance in the game.关键词
藏久棋/先验知识/蒙特卡洛树搜索/深度强化学习/策略选择优化函数/评估函数Key words
Tibetan Jiu Chess/prior knowledge/Monte Carlo tree search/deep reinforcement learning/strategy selection optimization function/evaluation function分类
信息技术与安全科学引用本文复制引用
王亚杰,谷峰,刘松,杨静怡,王世鹏..融合先验知识的藏久棋MCTS算法优化[J].沈阳航空航天大学学报,2025,42(4):59-67,9.基金项目
中国科协科普提升类项目(项目编号:KXYJS2022092). （项目编号:KXYJS2022092）
