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There is increasing awareness in the planning community that the burden ofspecifying complete domain models is too high, which impedes the applicabilityof planning technology in many real-world domains. Although there have manylearning systems that help automatically learning domain models, most existingwork assumes that the input traces are completely correct. A more realisticsituation is that the plan traces are disordered and noisy, such as plan tracesdescribed by natural language. In this paper we propose and evaluate anapproach for doing this. Our approach takes as input a set of plan traces withdisordered actions and noise and outputs action models that can best explainthe plan traces. We use a MAX-SAT framework for learning, where the constraintsare derived from the given plan traces. Unlike traditional action modelslearners, the states in plan traces can be partially observable and noisy aswell as the actions in plan traces can be disordered and parallel. Wedemonstrate the effectiveness of our approach through a systematic empiricalevaluation with both IPC domains and the real-world dataset extracted fromnatural language documents.

Learning Action Models from Disordered and Noisy Plan Traces