An integrated environment for knowledge acquisition
Proceedings of the 6th international conference on Intelligent user interfaces
Machine Learning
Learning action models from plan examples using weighted MAX-SAT
Artificial Intelligence
Transferring Knowledge from Another Domain for Learning Action Models
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in 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
Learning complex action models with quantifiers and logical implications
Artificial Intelligence
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AI planning requires action models to be given in advance. However, it is both time consuming and tedious for a human to encode the action models by hand using a formal language such as PDDL, as a result, learning action models is important for AI planning. On the other hand, the data being used to learn action models are often limited in planning domains, which makes the learning task very difficult. In this paper, we present a new algorithm to learn action models from plan traces by transferring useful information from other domains whose action models are already known. We present a method of building a metric to measure the shared information and transfer this information according to this metric. The larger the metric is, the bigger the information is transferred. In the experiment result, we show that our proposed algorithm is effective.