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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, the authors present a novel on-line case-based planning architecture that addresses some of these problems. The architecture addresses is-sues of plan acquisition, on-line plan execution, interleaved planning and execution and on-line plan adaptation. They also introduce the Darmok system, which implements this architecture in order to play Wargus (an open source clone of the well-known RTS gameWarcraft II). They present empirical evaluation of the performance of Darmok and show that it successfully learns to play the Wargus game.
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