Monte Carlo Tree Search

Peter I Cowling (Developer), Edward Jack Powley (Developer), Daniel Whitehouse (Developer)

Research output: Non-textual formSoftware


Monte Carlo Tree Search (MCTS) is a method for making optimal decisions in artificial intelligence (AI) problems, typically move planning in combinatorial games. It combines the generality of random simulation with the precision of tree search.
Research interest in MCTS has risen sharply due to its spectacular success with computer Go and potential application to a number of other difficult problems. Its application extends beyond games, and MCTS can theoretically be applied to any domain that can be described in terms of {state, action} pairs and simulation used to forecast outcomes.
Original languageEnglish
Publication statusPublished - 2012

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