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A Randomized Game-Tree Search Algorithm for Shogi Based on Bayesian Approach

  • Daisaku Yokoyama
  • Masaru Kitsuregawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8862)

Abstract

We propose a new randomized game-tree search algorithm based on Bayesian Approach. It consists of two main concepts; (1) using multiple game-tree search with a randomized evaluation function as simulations, (2) treating evaluated values as probability distribution and propagating it through the game-tree using the Bayesian Approach concept. Proposed method is focusing on applying to tactical games such as Shogi, in which MCTS is not currently effective. We apply the method for Shogi using a top-level computer player application which is constructed with many domain-specific search techniques. Through large amount of self-play evaluations, we conclude our method can achieve good win ratio against an ordinary game-tree search based player when enough computing resource is available. We also precisely examine performance behaviors of the method, and depict designing directions.

Keywords

Randomized Search Monte-Carlo Tree Search Game-tree Search Bayesian Approach 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Daisaku Yokoyama
    • 1
  • Masaru Kitsuregawa
    • 2
    • 1
  1. 1.Institute of Industrial ScienceThe University of TokyoJapan
  2. 2.National Institute of InformaticsJapan

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