Single-Player Monte-Carlo Tree Search

被引：0

作者：

Schadd, Maarten P. D. ^{[1
]}

Winands, Mark H. M. ^{[1
]}

van den Herik, H. Jaap ^{[1
]}

Chaslot, Guillaume M. J. -B. ^{[1
]}

Uiterwijk, Jos W. H. M. ^{[1
]}

机构：

[1] Univ Maastricht, Fac Humanities & Sci, MICC, Games & AI Grp, Maastricht, Netherlands

来源：

COMPUTERS AND GAMES | 2008年 / 5131卷

关键词：

D O I：

10.1007/978-3-540-87608-3_1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Classical methods such as A* and IDA* are a popular and successful choice for one-player games. However, they fail without an accurate admissible evaluation function. In this paper we investigate whether Monte-Carlo Tree Search (MCTS) is an interesting alternative for one-player games where A* and IDA* methods do not perform well. Therefore, we propose a new MCTS variant, called Single-Player Monte-Carlo Tree Search (SP-MCTS). The selection and backpropagation strategy in SP-MCTS are different from standard MCTS. Moreover, SP-MCTS makes use of a straightforward Meta-Search extension. We tested the method on the puzzle SameGame. It turned out that our SP-MCTS program gained the highest score so far on the standardized test set.

引用

页码：1 / +

页数：3

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