Belief-state Monte-Carlo tree search for Phantom games

Jiao Wang, Tan Zhu, Hongye Li, Chu-Hsuan Hsueh, I-Chen Wu
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引用次数: 7

Abstract

Playing games with imperfect information is a very challenging issue in AI field due to its high complexity. Phantom game is a kind of such games, which usually has a large game-tree complexity and has little research achievements until now. In Phantom games, rational human players commonly select actions according to their beliefs in the game, which can be represented as a concept of belie f-state. To the best of our knowledge, our paper is the first article to incorporate belief-states in the Monte-Carlo Tree Search, and the proposed algorithm is named BS-MCTS (Belief-state Monte-Carlo Tree Search). In BS-MCTS, a belief-state tree, in which each node is a belief-state, is constructed and the search procedure is in accordance with beliefs updated by heuristic search information. We also present two novel implementations in the belief learning, that are Opponent Guessing and Opponent Predicting, concerning the probability on the possible states and on future actions of the opponent respectively. To prove the effectiveness of our algorithm, BS-MCTS is applied to Phantom Tic-Tac-Toe and Phantom Go against other Monte-Carlo methods. The experimental results demonstrate that our method is outstanding and advanced. Moreover, based on BS-MCTS, our Phantom Go program had consecutively won three championships in Chinese National Tournaments.
幻影游戏的信念状态蒙特卡洛树搜索
在人工智能领域,不完全信息博弈是一个非常具有挑战性的问题,因为它具有很高的复杂性。幻影游戏就是此类游戏的一种,通常具有较大的游戏树复杂度,迄今为止研究成果较少。在Phantom游戏中,理性的人类玩家通常根据他们在游戏中的信念来选择行动,这可以用信念状态的概念来表示。据我们所知,我们的论文是第一篇将信念状态纳入蒙特卡罗树搜索的文章,提出的算法被命名为BS-MCTS(信念状态蒙特卡罗树搜索)。在BS-MCTS中,构建了一个信念状态树,每个节点都是一个信念状态,并根据启发式搜索信息更新的信念进行搜索。我们还提出了两种新的信念学习实现,即对手猜测和对手预测,分别涉及对手可能状态和未来动作的概率。为了证明该算法的有效性,将BS-MCTS应用于幻影井字棋和幻影围棋中,与其他蒙特卡罗方法进行对比。实验结果证明了该方法的优越性和先进性。此外,基于BS-MCTS,我们的幻影围棋项目在中国全国锦标赛中连续三次获得冠军。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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