A Randomized Game-Tree Search Algorithm for Shogi Based on Bayesian Approach
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.
KeywordsRandomized Search Monte-Carlo Tree Search Game-tree Search Bayesian Approach
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