Learning STRIPS Operators from Noisy and Incomplete Observations

Kira Mourao, Luke Zettlemoyer, Ron Petrick, Mark Steedman

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Agents learning to act autonomously in real world domains must acquire a model of the dynamics of the domain in which they operate. Learning domain dynamics can be challenging, especially where an agent only has partial access to the world state, and/or noisy external sensors. Even in standard STRIPS domains, existing approaches cannot learn from noisy, incomplete observations typical of real-world domains. We propose a method which learns STRIPS action models in such domains, by decomposing the problem into first learning a transition function between states in the form of a set of classifiers, and then deriving explicit STRIPS rules from the classifiers’ parameters. We evaluate our approach on simulated standard planning domains from the International Planning Competition, and show that it learns useful domain descriptions from noisy, incomplete observations.
Original languageEnglish
Title of host publicationProceedings of the Twenty Eighth Conference on Uncertainty in Artificial Intelligence (UAI 2012)
Number of pages10
Publication statusPublished - 2012


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