十六进制中更强的虚拟连接

Jakub Pawlewicz, R. Hayward, Philip Henderson, B. Arneson
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引用次数: 13

摘要

对于像Hex或Y或Havannah这样的连接游戏,找到有保证的单元到单元的连接策略可能是一个计算瓶颈。在自动玩家和求解器中,这种虚拟连接的集合通常是用Anshelevich的h搜索算法找到的:初始化平凡的连接,然后反复应用and规则(用于串联连接的组合)和or规则(用于并行连接的组合)。我们提出了FastVC搜索算法,这是一种寻找这种连接的新算法。当快速找到一组有代表性的连接比缓慢地找到一组更大的连接更重要时,FastVC搜索比h搜索更有效。我们在alpha-beta玩家Wolve、Monte Carlo树搜索玩家MoHex和证明数搜索实现Solver中测试了FastVC Search。它并没有强化《狼》,但却显著强化了《MoHex》和《Solver》。
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Stronger Virtual Connections in Hex
For connection games such as Hex or Y or Havannah, finding guaranteed cell-to-cell connection strategies can be a computational bottleneck. In automated players and solvers, sets of such virtual connections are often found with Anshelevich's H-search algorithm: initialize trivial connections, and then repeatedly apply an AND-rule (for combining connections in series) and an OR-rule (for combining connections in parallel). We present FastVC Search, a new algorithm for finding such connections. FastVC Search is more effective than H-search when finding a representative set of connections quickly is more important than finding a larger set of connections slowly. We tested FastVC Search in an alpha-beta player Wolve, a Monte Carlo tree search player MoHex, and a proof number search implementation called Solver. It does not strengthen Wolve, but it significantly strengthens MoHex and Solver.
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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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