A general architecture for maintaining a symbolic representation of the semantic game state Planners can be used to manage game content such as interactive narratives and agent behaviour. However, planners require a semantic representation of games objects and events to reason about them. This paper describes a framework to create a symbolic representation of the semantic content of a realtime game for use with a story generation planner. Asynchronous events from the game are marshalled by an event handler into messages that are passed to a domainspecific messagehandler. This message handler interprets and converts these messages to firstorder predicates for use in a domainindependent planner. This approach aims to limit the amount of semantic data stored within the game that is unnecessary for gameplay logic.

Cases and Clusters in Reuse Policies for DecisionMaking in Card Games 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 Casebased Reasoning (CBR) cardplaying agents. With the support of the KMEANS clustering algorithm, clustering results detailing problem statessituations 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 twostep 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, cardplaying 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.