Hanna Pasula
Luke Zettlemoyer
Leslie Pack Kaelbling
Abstract: To learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilistic STRIPS-like planning operators from examples. We demonstrate the effective learning of rule-based operators for a wide range of traditional planning domains.
Appeared in: Proceedings of the Fourteenth International Conference on Automated Planning and Scheduling.
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