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4*4-Pattern and Bayesian Learning in Monte-Carlo Go

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7168)

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.

Keywords

  • Anchor Point
  • Post Probability
  • Occupancy Rate
  • Bayesian Learning
  • Multiple Match

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The material in this paper is based upon work supported by the NSFC-MSRA Joint Research Fund under Grant 60971057.

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© 2012 Springer-Verlag Berlin Heidelberg

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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

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31865-8

  • Online ISBN: 978-3-642-31866-5

  • eBook Packages: Computer ScienceComputer Science (R0)