Opponent Type Adaptation for Case-Based Strategies in Adversarial Games

Abstract

We describe an approach for producing exploitive and adaptive case-based strategies in adversarial games. We describe how adaptation can be applied to a precomputed, static case-based strategy in order to allow the strategy to rapidly respond to changes in an opponent’s playing style. The exploitive strategies produced by this approach tend to hover around a precomputed solid strategy and adaptation is applied directly to the precomputed strategy once enough information has been gathered to classify the current opponent type. The use of a precomputed, seed strategy avoids performance degradation that can take place when little is known about an opponent. This allows our approach an advantage over other exploitive strategies whose playing decisions rely on large, individual opponent models constructed from scratch. We evaluate the approach within the experimental domain of two-player Limit Texas Hold’em poker.