Information Set Monte Carlo Tree Search

Monte Carlo tree search (MCTS) is an AI technique that has been successfully applied to many deterministic games of perfect information. This paper investigates the application of MCTS methods to games with hidden information and uncertainty. In particular, three new information set MCTS (ISMCTS) al...

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Bibliographic Details
Published inIEEE transactions on computational intelligence and AI in games. Vol. 4; no. 2; pp. 120 - 143
Main Authors Cowling, P. I., Powley, E. J., Whitehouse, D.
Format Journal Article
LanguageEnglish
Published IEEE 01.06.2012
Subjects
Algorithm design and analysis
Algorithms
Artificial intelligence
Artificial intelligence (AI)
Computational modeling
Decision making
game tree search
Games
hidden information
Intelligence
Monte Carlo methods
Monte Carlo tree search (MCTS)
Searching
Trees
Uncertainty
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Summary:Monte Carlo tree search (MCTS) is an AI technique that has been successfully applied to many deterministic games of perfect information. This paper investigates the application of MCTS methods to games with hidden information and uncertainty. In particular, three new information set MCTS (ISMCTS) algorithms are presented which handle different sources of hidden information and uncertainty in games. Instead of searching minimax trees of game states, the ISMCTS algorithms search trees of information sets, more directly analyzing the true structure of the game. These algorithms are tested in three domains with different characteristics, and it is demonstrated that our new algorithms outperform existing approaches to handling hidden information and uncertainty in games.
Bibliography:ObjectType-Article-2
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ISSN:1943-068X
1943-0698
DOI:10.1109/TCIAIG.2012.2200894