Learning Probabilistic Relational Planning Rules

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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.

Hanna Pasula, Luke S. Zettlemoyer, Leslie Pack Kae

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Artificial Intelligence | Complex Domains | KR 2004 | STRIPS-like Planning Operators | Traditional Planning Domains |

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Added	02 Jul 2010
Updated	02 Jul 2010
Type	Conference
Year	2004
Where	KR
Authors	Hanna Pasula, Luke S. Zettlemoyer, Leslie Pack Kaelbling

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Learning Probabilistic Relational Planning Rules

Artificial Intelligence | Complex Domains | KR 2004 | STRIPS-like Planning Operators | Traditional Planning Domains |

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