Move Prediction in Go with the Maximum Entropy Method

Nobuo Araki, Kazuhiro Yoshida, Yoshimasa Tsuruoka, Jun'ichi Tsujii
2007 2007 IEEE Symposium on Computational Intelligence and Games  
We address the problem of predicting moves in the board game of Go. We use the relative frequencies of local board patterns observed in game records to generate a ranked list of moves, and then apply the maximum entropy method (MEM) to the list to re-rank the moves. Move prediction is the task of selecting a small number of promising moves from all legal moves, and move prediction output can be used to improve the efficiency of the game tree search. The MEM enables us to make use of multiple
more » ... rlapping features, while avoiding problems with data sparseness. Our system was trained on 20000 expert games and had 33.9% prediction accuracy in 500 expert games.
doi:10.1109/cig.2007.368097 dblp:conf/cig/ArakiYTT07 fatcat:fn3qqthmfjcoxct4mew7jeqmeu