Pruning algorithms for multi-model adversary search

Abstract

The multi-model search framework generalizes minimax to allow exploitation of recursive opponent models. In this work we consider adding pruning to the multi-model search. We prove a sufficient condition that enables pruning and describe two pruning algorithms, αβ ∗ and αβ 1p ∗ . We prove correctness and optimality of the algorithms and provide an experimental study of their pruning power. We show that for opponent models that are not radically different from the player's strategy, the pruning power of these algorithms is significant.