Building Opponent Model in Imperfect Information

In imperfect information problems, board game is a class of special problem that differs from card games like poker. Several characters make it a valuable test bed for opponent modeling, which is one of the most difficult problems in artificial intelligence decision systems. In card games, opponent modeling has proved its importance on improving agents ’ strength. In this paper, a method of building opponent models in imperfect information board games is introduced systematically. Formalized evaluation method of arrangement strength in board game in proposed. Risk dominance is suggest as the classified criterion of building opponent models and basing that, classified primary probability tables are built as opponent models. Last, Siguo game is chosen as the application for opponent modeling and shows its advantages in the experiments