Learning Probabilistic Hierarchical Task Networks from Plan Examples to Capture User Preference
نویسندگان
چکیده
Hierarchical task networks provide an efficient way to encode user prescriptions about what constitute good plans using component methods. However, manual construction of these methods is complex and time consuming. In this paper, we propose a novel approach to learning probabilistic hierarchical task networks that capture the user preference by examining user-produced plans given no prior information about the methods. We introduce a theoretical basis to formally state the schema learning problem. We then make the connection between probabilistic grammar induction and probabilistic hierarchical task network learning, and propose a learning algorithm that shares ideas with probabilistic gram-
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Learning Probabilistic Hierarchical Task Networks to Capture User Preferences
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