Abstract
Most past research in modelling poker arises from the field of artificial intelligence, based on the many rules and probabilities involved in the game. The present study examines poker from a new perspective: applying knowledge elicitation techniques to model some of the decision making processes of expert poker players in post-flop betting. Based on data obtained through observations and interviews, using expert players as study participants, a decision making model is proposed, together with an abstraction hierarchy model. It was found that poker players create a set of mental models of their opponents, the active game situation, and of themselves as perceived by their opponents, in order to achieve the purposes of always making better decisions than their opponent and, whenever possible, maximizing the consequences of the opponent's mistakes. A set of strategies that are independent of specific situations and individual players were also discovered in this study.
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