Abstract
The paper proposes a new model of pattern, namely the 4*4-Pattern, to improve MCTS (Monte-Carlo Tree Search) in computer Go. A 4*4-Pattern provides a larger coverage space and more essential information than the original 3*3-Pattern. Nevertheless the latter is currently widely used. Due to the lack of a central symmetry, it takes greater challenges to apply a 4*4-Pattern compared to a 3*3-Pattern. Many details of a 4*4-Pattern implementation are presented, including classification, multiple matching, coding sequences, and fast lookup. Additionally, Bayesian 4*4-Pattern learning is introduced, and 4*4-Pattern libraries are automatically generated from a vast amount of professional game records. The results of our experiments show that the use of 4*4-Patterns can improve MCTS in 19*19 Go to some extent, in particular when supported by 4*4-Pattern libraries generated by Bayesian learning.
The material in this paper is based upon work supported by the NSFC-MSRA Joint Research Fund under Grant 60971057.
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Wang, J., Li, S., Chen, J., Wei, X., Lv, H., Xu, X. (2012). 4*4-Pattern and Bayesian Learning in Monte-Carlo Go. In: van den Herik, H.J., Plaat, A. (eds) Advances in Computer Games. ACG 2011. Lecture Notes in Computer Science, vol 7168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31866-5_10
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DOI: https://doi.org/10.1007/978-3-642-31866-5_10
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