Monte-Carlo Tree Search Solver
Monte-Carlo Tree Search Solver
复制标题
蒙特卡罗树搜索求解器
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Jahn
中科院分区：
文献类型：
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作者：
M. Winands;Y. Björnsson;Jahn
近年来，蒙特卡罗树搜索(MCTS)在计算机围棋领域取得了长足的进步。在本文中，我们研究了MCTS在游戏行动线(LOA)中的应用。一个新的MCTS变种，称为MCTS-Solver，被设计成在LOA等猝死游戏中更好地发挥狭窄的战术路线。该算法在反向传播和选择策略上不同于传统的MCTS。只要有足够的时间，它就能证明头寸的博弈论价值。实验表明，使用MCTS-Solver的蒙特卡罗LOA程序以65%的胜率击败了使用MCTS的程序。此外，在几个不同版本的世界级β程序mia上，mcts-solver的性能比使用mcts的程序要好得多。因此，MCTS-Solver在使用基于模拟的搜索方法方面取得了真正的进步，大大改进了基于MCTS的程序。
Recently, Monte-Carlo Tree Search (MCTS) has advanced the field of computer Go substantially. In this article we investigate the application of MCTS for the game Lines of Action (LOA). A new MCTS variant, called MCTS-Solver, has been designed to play narrow tactical lines better in sudden-death games such as LOA. The variant differs from the traditional MCTS in respect to backpropagation and selection strategy. It is able to prove the game-theoretical value of a position given sufficient time. Experiments show that a Monte-Carlo LOA program using MCTS-Solver defeats a program using MCTS by a winning score of 65%. Moreover, MCTS-Solver performs much better than a program using MCTS against several different versions of the world-class ?βprogram MIA. Thus, MCTS-Solver constitutes genuine progress in using simulation-based search approaches in sudden-death games, significantly improving upon MCTS-based programs.