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A Survey of Monte Carlo Tree Search Methods

Browne, Cameron B and Powley, Edward and Whitehouse, Daniel and Lucas, Simon M and Cowling, Peter I and Rohlfshagen, Philipp and Tavener, Stephen and Perez, Diego and Samothrakis, Spyridon and Colton, Simon (2012) 'A Survey of Monte Carlo Tree Search Methods.' IEEE Transactions on Computational Intelligence and AI in Games, 4 (1). pp. 1-43. ISSN 1943-068X

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Abstract

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.

Item Type: Article
Uncontrolled Keywords: Artificial intelligence (AI); bandit-based methods; computer Go; game search; Monte Carlo tree search (MCTS); upper confidence bounds (UCB); upper confidence bounds for trees (UCT)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 19 Oct 2012 21:51
Last Modified: 15 Jan 2022 00:32
URI: http://repository.essex.ac.uk/id/eprint/4117

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