An integrated environment for knowledge acquisition
Proceedings of the 6th international conference on Intelligent user interfaces
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Machine Learning
Learning action models from plan examples using weighted MAX-SAT
Artificial Intelligence
Mapping and revising Markov logic networks for transfer learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Transfer Learning Action Models by Measuring the Similarity of Different Domains
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Transfer Learning beyond Text Classification
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Learning complex action models with quantifiers and logical implications
Artificial Intelligence
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Learning action models is an important and difficult task for AI planning, since it is both time-consuming and tedious for a human to encode the action models by hand using a formal language such as PDDL. In this paper, we present a new algorithm to learn action models from plan traces by transferring useful knowledge from another domain whose action models are already known. We call this algorithm t -LAMP , (transfer Learning Action Models from Plan traces) which can learn action models in PDDL language with quantifiers from plan traces where the intermediate states can contain noise and partial information. We apply Markov Logic Network to enable knowledge transfer, and show that using the transfer learning framework, the quality of the learned action models are generally better than the case when not using an existing domain for transfer.