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Some domains, such as real-time strategy (RTS) games, pose several challenges to traditional planning and machine learning techniques. In this paper, we present a novel on-line case-based planning architecture that addresses some of these problems. Our architecture addresses issues of plan acquisition, on-line plan execution, interleaved planning and execution and on-line plan adaptation. We also introduce the Darmok system, which implements this architecture in order to play Wargus (an open source clone of the well-known RTS game Warcraft II). We present empirical evaluation of the performance of Darmok and show that it successfully learns to play the Wargus game.

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Abstract. Case-Based Planning (CBP) is an effective technique for solving planning problems that has the potential to reduce the computational complexity of the generative planning approaches [8,3]. However, the success of plan execution using CBP depends highly on the selection of a correct plan; especially when the case-base of plans is extensive. In this paper we introduce the concept of a situation and explain a situation assessment algorithm which improves plan retrieval for CBP. We have applied situation assessment to our previous CBP system, Darmok [11], in the domain of real-time strategy games. During Darmok's execution using situation assessment, the high-level representation of the game state i.e. situation is predicted using a decision tree based SituationClassification model. Situation predicted is further used for the selection of relevant knowledge intensive features, which are derived from the basic representation of the game state, to compute the similarity of cases with the current problem. The feature selection performed here is knowledge based and improves the performance of similarity measurements during plan retrieval. The instantiation of the situation assessment algorithm to Darmok gave us promising results for plan retrieval within the real-time constraints.

Natural Language Generation for personality rich characters represents one of the important directions for believable agents research. The typical approach to interactive NLG is to hand-author textual responses to different situations. In this paper we address NLG for interactive games. Specifically, we present a novel template-based system that provides two distinct advantages over existing systems. First, our system not only works for dialogue, but enables a character's personality and emotional state to influence the feel of the utterance. Second, our templates are resuable across characters, thus decreasing the burden on the game author. We briefly describe our system and present results of a preliminary evaluation study.