Single-player Monte-Carlo tree search

Maarten P D Schadd, Mark H M Winands, H. Jaap van den Herik, Guillaume M.J.B Chaslot

Research output: Contribution to conferenceAbstractAcademic

28 Citations (Scopus)

Abstract

Classical methods such as A* and IDA* are a popular and successful choice for one-player games. However, they fail without an accurate admissible evaluation function. In this paper 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 a straightforward Meta-Search extension. We tested the method on the puzzle SameGame. It turned out that our SP-MCTS program gained the highest score so far on the standardized test set. © 2008 Springer-Verlag Berlin Heidelberg.
Original languageEnglish
Pages361-362
Number of pages2
Publication statusPublished - 2008
Event Twentieth Belgian-Dutch Artificial Intelligence Conference -
Duration: 30 Oct 200831 Oct 2008

Conference

Conference Twentieth Belgian-Dutch Artificial Intelligence Conference
Period30/10/0831/10/08

Cite this

Schadd, M. P. D., Winands, M. H. M., van den Herik, H. J., & Chaslot, G. M. J. B. (2008). Single-player Monte-Carlo tree search. 361-362. Abstract from Twentieth Belgian-Dutch Artificial Intelligence Conference, .