Action-model acquisition from noisy plan traces

Hankz Hankui Zhuo, Subbarao Kambhampati

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

38 Scopus citations


There is increasing awareness in the planning community that the burden of specifying complete domain models is too high, which impedes the applicability of planning technology in many real-world domains. Although there have been many learning approaches that help automatically creating domain models, they all assume plan traces (training data) are correct. In this paper, we aim to remove this assumption, allowing plan traces to be with noise. Compared to collecting large amount of correct plan traces, it is much easier to collect noisy plan traces, e.g., we can directly exploit sensors to help collect noisy plan traces. We consider a novel solution for this challenge that can learn action models from noisy plan traces. We create a set of random variables to capture the possible correct plan traces behind the observed noisy ones, and build a graphical model to describe the physics of the domain. We then learn the parameters of the graphical model and acquire the domain model based on the learnt parameters. In the experiment, we empirically show that our approach is effective in several planning domains.

Original languageEnglish (US)
Title of host publicationIJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
Number of pages7
StatePublished - 2013
Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China
Duration: Aug 3 2013Aug 9 2013

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823


Other23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

ASJC Scopus subject areas

  • Artificial Intelligence


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