Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Automatically Composed Workflows for Grid Environments
IEEE Intelligent Systems
Learning action models from plan examples using weighted MAX-SAT
Artificial Intelligence
Learning planning rules in noisy stochastic worlds
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning symbolic models of stochastic domains
Journal of Artificial Intelligence Research
New inference rules for Max-SAT
Journal of Artificial Intelligence Research
A general model for online probabilistic plan recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning partially observable deterministic action models
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Automated composition of Web services via planning in asynchronous domains
Artificial Intelligence
Learning complex action models with quantifiers and logical implications
Artificial Intelligence
Learning high-level planning from text
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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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.