Incorporating Opponent Models into Adversary Search

This work presents a generalized theoretical framework that allows incorporation of opponent models into adversary search. We present the M∗ algorithm, a generalization of minimax that uses an arbitrary opponent model to simulate the opponent’s search. The opponent model is a recursive structure consisting of the opponent’s evaluation function and its model of the player. We demonstrate experimentally the potential benefit of using an opponent model. Pruning in M∗ is impossible in the general case. We prove a sufficient condition for pruning and present the αβ∗ algorithm which returns the M∗ value of a tree while searching only necessary branches.