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http://dx.doi.org/10.7583/JKGS.2018.18.6.59

The most promising first moves on small Go boards, based on pure Monte-Carlo Tree Search  

Lee, Byung-Doo (Department of Baduk Studies, Division of Sports Science, Sehan University)
Publication Information
Journal of Korea Game Society / v.18, no.6, 2018 , pp. 59-68 More about this Journal
Abstract
In spite of its simple rule, Go is one of the most complex strategic board games in the field of Artificial Intelligence (AI). Monte-Carlo Tree Search (MCTS) is an algorithm with best-first tree search, and has used to implement computer Go. We try to find the most promising first move using MCTS for playing a Go game on a board of size smaller than $9{\times}9$ Go board. The experimental result reveals that MCTS prefers to place the first move at the center in case of odd-sized Go boards, and at the central in case of even-sized Go boards.
Keywords
small Go board; Monte-Carlo Tree Search; promising first move; odd- and even-sized Go boards;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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