A general explanation-based learning mechanism and its application to narrative understanding
A general explanation-based learning mechanism and its application to narrative understanding
A formal theory of plan recognition and its implementation
Reasoning about plans
CLIP: concept learning from inference patterns
Artificial Intelligence - Special issue: AI research in Japan
Bayesian Models for Keyhole Plan Recognition in an Adventure Game
User Modeling and User-Adapted Interaction
Selectively generalizing plans for problem-solving
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Programming by demonstration: an inductive learning formulation
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
Learning hierarchical task models by defining and refining examples
Proceedings of the 1st international conference on Knowledge capture
Techniques for Plan Recognition
User Modeling and User-Adapted Interaction
Goal recognition through goal graph analysis
Journal of Artificial Intelligence Research
From interaction data to plan libraries: a clustering approach
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Case-based plan recognition in computer games
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Flexible goal recognition via graph construction and analysis
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Generating artificial corpora for plan recognition
UM'05 Proceedings of the 10th international conference on User Modeling
HYREC: a hybrid recommendation system for e-commerce
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Modeling sequences of user actions for statistical goal recognition
User Modeling and User-Adapted Interaction
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While most plan recognition systems make use of a plan library that contains the set of available plan hypotheses, little effort has been devoted to the question of how to create such a library. This problem is particularly difficult to deal with when only little domain knowledge is available-a common situation when e.g. developing a help system for an already existing software system. This paper describes how operational decompositions of plans can be extracted from a set of sample action sequences, thus providing the basis for automating the acquisition of plan libraries. Efficient algorithms for the approximation of optimal decompositions and experimental results supporting their feasibility are presented.