Case-based strategies in computer poker
The state-of-the-art within Artificial Intelligence has directly benefited from research conducted within the computer poker domain. One such success has been the advancement of bottom up equilibrium finding algorithms via computational game theory. On the other hand, alternative top down approaches, that attempt to generalise decisions observed within a collection of data, have not received as much attention. In this work we employ a top down approach in order to construct case-based strategies within three computer poker domains. Our analysis begins within the simplest variation of Texas Hold'em poker, i.e. two-player, limit Hold'em. We trace the evolution of our case-based architecture and evaluate the effect that modifications have on strategy performance. The end result of our experimentation is a coherent framework for producing strong case-based strategies based on the observation and generalisation of expert decisions. The lessons learned within this domain offer valuable insights, that we use to apply the framework to the more complicated domains of two-player, no-limit Hold'em and multi-player, limit Hold'em. For each domain we present results obtained from the Annual Computer Poker Competition, where the best poker agents in the world are challenged against each other. We also present results against human opposition.
imperfect information games game ai case-based reasoning