Cases and Clusters in Reuse Policies for Decision-Making in Card Games

Abstract

This work investigates the combination of cases and clusters in the reuse of game actions (e.g., cards played, bets made) recorded in the cases retrieved for a given query in Case-based Reasoning (CBR) card-playing agents. With the support of the K-MEANS clustering algorithm, clustering results detailing problem states/situations and game outcomes relationships recorded in cases from the case base guide the execution of augmented reuse policies. These policies consider the game actions recorded in the retrieved cases in the selection of the clusters to be used. Then, the cases that belong to the selected clusters are used in the determination of which game action is reused as a solution to the current game problem situation. With this two-step reuse process, the proposed policies rely on the majority with clusters, the probability with clusters, the number of points won with clusters and the chance of victory with clusters. To evaluate these proposals, card-playing agents implemented with different reuse policies competed against each other in duplicated game matches where all of them played using the same set of cards.