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

  • Jiao Wang
  • Shiyuan Li
  • Jitong Chen
  • Xin Wei
  • Huizhan Lv
  • Xinhe Xu
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jiao Wang
    • 1
  • Shiyuan Li
    • 1
  • Jitong Chen
    • 1
  • Xin Wei
    • 1
  • Huizhan Lv
    • 1
  • Xinhe Xu
    • 1
  1. 1.College of Information Science and EngineeringNortheastern UniversityChina

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