Enhancing search efficiency by using move categorization based on game progress in Amazons

Abstract

Amazons is a two-player perfect information game with a high branching factor, particularly in the opening. Therefore, improving the efficiency of the search is important for improving the playing strength of an Amazons program. In this paper we propose a new method for improving search in Amazons by using move categories to order moves. The move order is decided by the likelihood of the move actually being selected as the best move. Furthermore, it will be shown that the likelihood of move selection strongly depends upon the stage of the game. Therefore, our method is further refined by adjusting the likelihood of moves according to the progress of the game. Self-play experiments show that using move categories significantly improves the strength of an Amazons program and that combining move categories with game progress is better than using only move categories. © 2006 Springer-Verlag.