Browse > Article
http://dx.doi.org/10.7583/JKGS.2016.16.4.79

Enhanced strategic Monte-Carlo Tree Search algorithm to play the game of Tic-Tac-Toe  

Lee, Byung-Doo (Dept. of Baduk Studies, Division of Sports Science, Sehan University)
Publication Information
Journal of Korea Game Society / v.16, no.4, 2016 , pp. 79-86 More about this Journal
Abstract
Monte-Carlo Tree Search(MCTS) is a best-first tree search algorithm and has been successfully applied to various games, especially to the game of Go. We evaluate the performance of MCTS playing against each other in the game of Tic-Tac-Toe. It reveals that the first player always has an overwhelming advantage to the second player; and we try to find out the reason why the first player is superior to the second player in spite of the fact that the best game result should be a draw. Since MCTS is a statistical algorithm based on the repeated random sampling, it cannot adequately tackle an urgent problem that needs a strategy, especially for the second player. For this, we propose a strategic MCTS(S-MCTS) and show that the S-MCTS player never loses a Tic-Tac-Toe game.
Keywords
Go; Tic-Tac-Toe; MCTS; S-MCTS; Monte-Carlo Tree Search;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
연도 인용수 순위
1 B.D. Lee, "Implementation of robust Tic-Tac-Toe game player, using enhanced Monte-Carlo algorithm", Journal of Korean Society for Computer Game, Vol. 28, No. 3, pp. 135-141, 2015.
2 B.D. Lee, D.S. Park and Y.W. Choi, "The UCT algorithm applied to find the best first move in the game of Tic-Tac-Toe", Journal of Korea Game Society, Vol. 15, No. 5, pp. 109-118, 2015.   DOI
3 B.D. Lee, "Competition between MCTS and UCT in the game of Tic-Tac-Toe", Journal of Korean Society for Computer Game, Vol. 29, No. 1, pp. 1-6, 2016.   DOI
4 B.D. Lee, "Analysis of Tic-Tac-Toe Game Strategies using Genetic Algorithm", Journal of Korea Game Society, Vol. 14, No. 6, pp. 39-48, 2014.
5 B.D. Lee, "Monte-Carlo Tree Search Applied to the Game of Tic-Tac-Toe", Journal of Korea Game Society, Vol. 14, No. 3, pp. 47-54, 2014.   DOI
6 D. Silver et al., "Mastering the game of Go with deep neural networks and tree search", Journal of Nature, Vol. 529, Issue 7587, pp. 484-489, 2016.   DOI
7 P.S. Jang "Overseas Innovation Trend", from http://www.stepi.re.kr, 2016.
8 B.D. Lee and Y.W. Choi, "The best move sequence in playing Tic-Tac-Toe game", Journal of The Korean Society for Computer Game, Vol. 27, No. 3, pp. 11-16, 2014.
9 B.D. Lee, "Evolutionary neural network model for recognizing strategic fitness of a finished Tic-Tac-Toe game", Journal of Korean Society for Computer Game, Vol. 28, No. 2, pp. 95-101, 2015.
10 S. Gelly, M. Schoenauer, M. Sebag, O. Teytaud, L. Kocsis, D. Silver and C. Szepesvari, "The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions", Communications of the ACM, Vol. 55, No. 3, pp. 106-113, 2012.
11 G. Chaslot, "Monte-Carlo Tree Search", Ph.D. dissertation, University of Masstricht, 2010.
12 Wikipedia, "Computer Go", from http://en.wikipedia.org/wiki/Computer_Go, 2016.
13 Wikipedia, "Tic-Tac-Toe", from http://en.wikipedia.org/wiki/Tic-Tac-Toe, 2016.
14 A.A.J van der Kleij, "Monte Carlo Tree Search and Opponent Modeling through Player Clustering in no-limit Texas Hold'en Poker", Master thesis, University of Groningen, 2010. http://en.wikipedia.org/wiki/Tic-Tac-Toe, 2016.
15 N. Sephton, P.I. Cowling, E. Powley and N.H. Slaven, "Heuristic Move Pruning in Monte Carlo Tree Search for the Strategic Card Game Lords of War", In Computational Intelligence and Games (CIG) of IEEE, pp. 1-7, 2014.
16 T. Pepels, "Novel Selection Methods for Monte-Carlo Tree Search", Master thesis, University of Masstricht, 2014.
17 D. Brand and S. Kroon, "Sample Evaluation for Action Selection in Monte Carlo Tree Search", from http://dl.acm.org/citation.cfm?doid=2664591.2664612, 2016.
18 Y. Wang and S. Gelly, "Modification of UCT and sequence-like simulations for Monte-Carlo Go", from http://dept.stat.lsa.umich.edu/-yizwang/publications/wang07modifications.pdf, 2016.