Move Prediction in Go with the Maximum Entropy Method

Abstract

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 overlapping 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