Journal
IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES
Volume 2, Issue 4, Pages 278-287Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCIAIG.2010.2096427
Keywords
Computer Go; majority voting; Monte Carlo tree search (MCTS); root parallelization; tree parallelization
Funding
- Japan Science and Technology Agency (JST)
- Japan Society for the Promotion of Science (JSPS) Global Centers of Excellence (COE)
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Parallelizing Monte Carlo tree search (MCTS) has been considered to be a way to improve the strength of Computer Go programs. In this paper, we analyze the performance of two root parallelization methods: the standard strategy based on average selection and our new strategy based on majority voting. As a starting code base, we used Fuego, which is one of the best programs available. Our experimental results with 64 central processing unit (CPU) cores show that majority voting outperforms average selection. Additionally, we show through an extensive analysis that root parallelization has limitations.
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