Learning STRIPS Operators from Noisy and Incomplete Observations

Kira Mourao, Luke S. Zettlemoyer, Ronald P. A. Petrick, Mark Steedman
Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, PMLR R10:612-621, 2012.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR10-mourao12a, title = {Learning {STRIPS} Operators from Noisy and Incomplete Observations}, author = {Mourao, Kira and Zettlemoyer, Luke S. and Petrick, Ronald P. A. and Steedman, Mark}, booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence}, pages = {612--621}, year = {2012}, editor = {de Freitas, Nando and Murphy, Kevin}, volume = {R10}, series = {Proceedings of Machine Learning Research}, month = {14--18 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r10/main/assets/mourao12a/mourao12a.pdf}, url = {https://proceedings.mlr.press/r10/mourao12a.html}, abstract = {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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Learning STRIPS Operators from Noisy and Incomplete Observations %A Kira Mourao %A Luke S. Zettlemoyer %A Ronald P. A. Petrick %A Mark Steedman %B Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2012 %E Nando de Freitas %E Kevin Murphy %F pmlr-vR10-mourao12a %I PMLR %P 612--621 %U https://proceedings.mlr.press/r10/mourao12a.html %V R10 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Mourao, K., Zettlemoyer, L.S., Petrick, R.P.A. & Steedman, M.. (2012). Learning STRIPS Operators from Noisy and Incomplete Observations. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R10:612-621 Available from https://proceedings.mlr.press/r10/mourao12a.html. Reissued by PMLR on 04 October 2026.

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