On the Construction, Maintenance and Analysis of Case-Based Strategies in Computer Poker

The state-of-the-art within Artificial Intelligence has directly benefited from research con-ducted within the computer poker domain. One such success has been the 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 col-lection of data, have not received as much attention. In this thesis we examine top down ap-proaches that use Case-Based Reasoning in order to construct strategies within the domain of computer poker. Our analysis begins with the development of frameworks to produce static strategies that do not change during game play. We trace the evolution of our case-based ar-chitecture 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 strate-gies based on the observation and generalisation of expert decisions. Next, we introduce three augmentation procedures that extend the initial frameworks in order to produce case-based strategies that are able to adapt to changing game conditions and exploit weaknesses of their opponents. Two of the augmentation procedures introduce different forms of opponent mod