Monte-Carlo tree search solver

Mark H M Winands*, Yngvi Bjornsson, Jahn Takeshi Saito

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic


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 tacti- cal 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 us- ing 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 αβ pro- gram MIA. Thus, MCTS-Solver constitutes genuine progress in using simulation-based search approaches in sudden-death games, significantly improving upon MCTS-based programs.
Original languageEnglish
Title of host publicationComputers and Games
Number of pages12
ISBN (Print)3540876073
Publication statusPublished - 2008

Publication series

SeriesLecture Notes in Computer Science

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