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Address correspondence to Santi Ontañón, CCL, Cognitive Computing Lab, Georgia Institute of Technology, Atlanta, GA 30322; e-mail:


Some domains, such as real-time strategy (RTS) games, pose several challenges to traditional planning and machine learning techniques. In this article, 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 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.