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 algorithm which returns the M value of a tree while searching only necessary branches.

• Type: Conference
• Year: 1996
• Where: AAAI
• Authors: David Carmel, Shaul Markovitch
• Keywords: AAAI 1996, Arbitrary Opponent Model, Intelligent Agents, Opponent Model, Opponent's Evaluation Function