中国黑棋残局中的等价类

Jr-Chang Chen, Ting-Yu Lin, Bo-Nian Chen, T. Hsu
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引用次数: 17

摘要

中国象棋是一种不确定的双人棋,目前还没有得到深入的研究。最先进的程序专注于使用搜索算法来探索在开局和游戏中期阶段翻转未暴露棋子的概率行为。关于开卷和残局数据库的研究相对较少,特别是具有不确定性的残局。在本文中,我们提出了一个等价关系,对每个玩家的材料组合之间的复杂块关系进行分类,并推导了所有这些材料组合的划分。该技术可以应用于终局数据库压缩,以减少需要构建的终局的数量。因此,计算时间和终端数据库的大小可以大大减少。此外,了解片段关系有助于开发设计良好的评价函数,提高了搜索效率。在中国黑棋中,仅由露出的棋子组成的非平凡材料组合的数量为8 497 176,包含至少一个未露出的棋子的数量为239 980 775 397。在该方法下,上述材料组合的压缩率分别达到28.93%和42.52%;如果将该方法应用于由3到8个棋子组成的终局,压缩率分别达到5.82%和5.98%。
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Equivalence Classes in Chinese Dark Chess Endgames
Chinese Dark Chess, a nondeterministic two-player game, has not been studied thoroughly. State-of-the-art programs focus on using search algorithms to explore the probability behavior of flipping unrevealed pieces in the opening and the midgame phases. There has been comparatively little research on opening books and endgame databases, especially endgames with nondeterministic flips. In this paper, we propose an equivalence relation that classifies the complex piece relations between the material combinations of each player, and derive a partition for all such material combinations. The technique can be applied to endgame database compression to reduce the number of endgames that need to be constructed. As a result, the computation time and the size of endgame databases can be reduced substantially. Furthermore, understanding the piece relations facilitates the development of a well-designed evaluation function and enhances the search efficiency. In Chinese Dark Chess, the number of nontrivial material combinations comprised of only revealed pieces is 8 497 176, and the number that contain at least one unrevealed piece is 239 980 775 397. Under the proposed method, the compression rates of the above material combinations reach 28.93% and 42.52%, respectively; if the method is applied to endgames comprised of three to eight pieces, the compression rates reach 5.82% and 5.98%, respectively.
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来源期刊
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.60
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.
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