Single-Player Monte-Carlo Tree Search for SameGame

Maarten P. D. Schadd*, Mark H. M. Winands, Mandy J. W. Tak, Jos W. H. M. Uiterwijk

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

32 Citations (Web of Science)

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.
Original languageEnglish
Pages (from-to)3-11
Number of pages9
JournalKnowledge-Based Systems
Volume34
DOIs
Publication statusPublished - Oct 2012

Keywords

  • Monte-Carlo tree search
  • One-player game
  • Puzzle
  • Same Game
  • Cross-entropy method

Cite this