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Agents learning to act autonomously in real-world domains must acquire amodel of the dynamics of the domain in which they operate. Learning domaindynamics can be challenging, especially where an agent only has partial accessto the world state, and/or noisy external sensors. Even in standard STRIPSdomains, existing approaches cannot learn from noisy, incomplete observationstypical of real-world domains. We propose a method which learns STRIPS actionmodels in such domains, by decomposing the problem into first learning atransition function between states in the form of a set of classifiers, andthen deriving explicit STRIPS rules from the classifiers' parameters. Weevaluate our approach on simulated standard planning domains from theInternational Planning Competition, and show that it learns useful domaindescriptions from noisy, incomplete observations.

Learning STRIPS Operators from Noisy and Incomplete Observations