Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
402403 | Knowledge-Based Systems | 2012 | 9 Pages |
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.