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Upper Confidence Bounds for Trees in the Game of Tic-Tac-Toe

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Monte-Carlo Tree Search (MCTS) is a best-first tree search algorithm to evaluate states and has been successfully applied to various games, especially to the game of Go. Nowadays, the most of MCTS research uses the Upper Confidence Bounds for Trees (UCT) algorithm, which is a variant of MCTS, to balance exploitation and exploration of states. We evaluate the performance of MCTS and UCT playing against each other in the two-player game of Tic-Tac-Toe. The experimental result shows that the UCT player is slightly better than the MCTS player.
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Keywords: MCTS; Monte-Carlo Tree Search; Tic-Tac-Toe; UCT; Upper Confidence Bounds for Trees

Document Type: Research Article

Affiliations: 1: Department of Sports Science, Sehan University, South Korea 2: Department of Information and Communication Engineering, Sungkyul University, South Korea

Publication date: November 1, 2016

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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