Learning Probabilistic Relational Planning Rules

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.

• Type: Conference
• Year: 2004
• Where: KR
• Authors: Hanna Pasula, Luke S. Zettlemoyer, Leslie Pack Kaelbling
• Keywords: Artificial Intelligence, Complex Domains, KR 2004, STRIPS-like Planning Operators, Traditional Planning Domains