Cowling, Peter I. orcid.org/0000-0003-1310-6683, Powley, Edward J. and Whitehouse, Daniel (2012) Information Set Monte Carlo Tree Search. Computational Intelligence and AI in Games, IEEE Transactions on. 6203567. pp. 120-143. ISSN 1943-068X
Abstract
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. Index Terms—Artificial intelligence (AI), game tree search, hidden information, Monte Carlo methods, Monte Carlo tree search (MCTS), uncertainty.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 28 Jun 2013 00:19 |
Last Modified: | 02 Apr 2025 23:05 |
Published Version: | https://doi.org/10.1109/TCIAIG.2012.2200894 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/TCIAIG.2012.2200894 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:75048 |
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Filename: CowlingPowleyWhitehouse2012.pdf
Description: Information Set Monte Carlo Tree Search