Although opponent modeling has been studied in perfect information games (for example [7]), the performance loss by ignoring it and assuming a perfect opponent is small, and hence it is usually ignored. In contrast, opponent modeling in poker can be the distinguishing feature between players at different skill levels. If a set of players all have a comparable knowledge of poker fundamentals, the ability to alter decisions based on an accurate model of the opponent may have a greater impact on success than any other strategic principle.
Deciding how to gather information about the opponents and how to use it to improve the quality of betting decisions is a complex and interesting problem. Loki-1's Opponent Modeler was a first attempt at making appropriate inferences from observing the opponents' actions and then applying them by changing betting decisions to exploit any identified pattern or weakness in the opponents' play. The Opponent Modeler uses the betting history of the opponents to determine a likely probability distribution for their hole cards which is used by the Hand Evaluator. Opponent modeling was experimentally shown to significantly improve Loki-1's performance [4].