Playout search for Monte-Carlo tree search in multi-player games

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Abstract

Over the past few years, Monte-Carlo Tree Search (MCTS) has become a popular search technique for playing multi-player games. In this paper we propose a technique called Playout Search. This enhancement allows the use of small searches in the playout phase of MCTS in order to improve the reliability of the playouts. We investigate max , Paranoid, and BRS for Playout Search and analyze their performance in two deterministic perfect-information multi-player games: Focus and Chinese Checkers. The experimental results show that Playout Search significantly increases the quality of the playouts in both games. However, it slows down the speed of the playouts, which outweighs the benefit of better playouts if the thinking time for the players is small. When the players are given a sufficient amount of thinking time, Playout Search employing Paranoid search is a significant improvement in the 4-player variant of Focus and the 3-player variant of Chinese Checkers. © 2012 Springer-Verlag.

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APA

Nijssen, J. A. M., & Winands, M. H. M. (2012). Playout search for Monte-Carlo tree search in multi-player games. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7168 LNCS, pp. 72–83). https://doi.org/10.1007/978-3-642-31866-5_7

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