Monte-Carlo tree search solver

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Abstract

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 αβ program MIA. Thus, MCTS-Solver constitutes genuine progress in using simulation-based search approaches in sudden-death games, significantly improving upon MCTS-based programs. © 2008 Springer-Verlag Berlin Heidelberg.

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APA

Winands, M. H. M., Björnsson, Y., & Saito, J. T. (2008). Monte-Carlo tree search solver. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5131 LNCS, pp. 25–36). https://doi.org/10.1007/978-3-540-87608-3_3

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