Single-player Monte-Carlo tree search for SameGame

  • Authors:

  • Maarten P. D. Schadd;Mark H. M. Winands;Mandy J. W. Tak;Jos W. H. M. Uiterwijk

  • Affiliations:

  • Games and AI Group, Department of Knowledge Engineering, Maastricht University, Maastricht, The Netherlands;Games and AI Group, Department of Knowledge Engineering, Maastricht University, Maastricht, The Netherlands;Games and AI Group, Department of Knowledge Engineering, Maastricht University, Maastricht, The Netherlands;Games and AI Group, Department of Knowledge Engineering, Maastricht University, Maastricht, The Netherlands

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2012

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Abstract

Classic methods such as A^* and IDA^* are a popular and successful choice for one-player games. However, without an accurate admissible evaluation function, they fail. In this article 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 randomized restarts. We tested IDA^* and SP-MCTS on the puzzle SameGame and used the cross-entropy method to tune the SP-MCTS parameters. It turned out that our SP-MCTS program is able to score a substantial number of points on the standardized test set.