While several researchers have applied case-based reasoning techniques to games, only Ponsen and Spronck (2004) have addressed the challenging problem of learning to win real-time games. Focusing on WARGUS, they report good results for a genetic algorithm that searches in plan space, and for a weighting algorithm (dynamic scripting) that biases subplan retrieval. However, both approaches assume a static opponent, and were not designed to transfer their learned knowledge to opponents with substantially different strategies. We introduce a plan retrieval algorithm that, by using three key sources of domain knowledge, removes the assumption of a static opponent. Our experiments show that its implementation in the Case-based Tactician (CAT) significantly outperforms the best among a set of genetically evolved plans when tested against random WARGUS opponents. CAT communicates with WARGUS through TIELT, a testbed for integrating and evaluating decision systems with simulators. This is the first application of TIELT. We describe this application, our lessons learned, and our motivations for future work.
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