A formal theory of plan recognition
A formal theory of plan recognition
A Bayesian model of plan recognition
Artificial Intelligence
Case-based reasoning
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Planning and Learning by Analogical Reasoning
Planning and Learning by Analogical Reasoning
Inside Case-Based Reasoning
Incremental Case-Based Plan Recognition Using State Indices
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
PRODIGY 4.0: The Manual and Tutorial
PRODIGY 4.0: The Manual and Tutorial
Case-based plan recognition in computer games
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
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Our research investigates a case-based approach to plan recognition using incomplete incrementally learned plan libraries. To learn plan libraries, one must be able to process novel input. Retrieval based on similarities among concrete planning situations rather than among planning actions enables recognition despite the occurrence of newly observed planning actions and states. In addition, we explore the benefits of predictions using a measure that we call abstract similarity. Abstract similarity is used when a concrete state maps to no known abstract state. Instead a search is performed for nearby abstract states based on a nearest neighbour technique. Such a retrieval scheme enables accurate prediction in light of extremely novel observed situations. The properties of retrieval in abstract state-spaces are investigated in three standard planning domains. We first determine optimal radii to use that determines a spherical sub-hyperspace that limits the search. Experimental results then show that significant improvements in the recognition process are obtained using abstract similarity.