Planning for conjunctive goals
Artificial Intelligence
A formal theory of plan recognition
A formal theory of plan recognition
Experience with a learning personal assistant
Communications of the ACM
Learning text analysis rules for domain-specific natural language processing
Learning text analysis rules for domain-specific natural language processing
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
A sound and fast goal recognizer
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Accounting for context in plan recognition, with application to traffic monitoring
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Bayesian Models for Keyhole Plan Recognition in an Adventure Game
User Modeling and User-Adapted Interaction
Techniques for Plan Recognition
User Modeling and User-Adapted Interaction
Programming by Demonstration Using Version Space Algebra
Machine Learning
Recognition of Users' Activities Using Constraint Satisfaction
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Domain Independent Goal Recognition
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Plan recognition in exploratory domains
Artificial Intelligence
Plan Recognition and Visualization in Exploratory Learning Environments
ACM Transactions on Interactive Intelligent Systems (TiiS)
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Because observing the same actions can warrant different conclusions depending on who executed the actions, a goal recognizer that works well on one person might not work well on another. Two problems that arise in providing user-specific recognition are how to consider the vast number of possible adaptations that might be made to the goal recognizer and how to evaluate a particular set of adaptations. For the first problem, we evaluate the use of hillclimbing to search the space of all combinations of an input set of adaptations. For the second problem, we present an algorithm that estimates the accuracy and coverage of a recognizer on a set of action sequences the individual has recently executed. We use these techniques to construct Adapt, a recognizer-independent unsupervised-learning algorithm for adapting a recognizer to a person's idiosyncratic behaviors. Our experiments in two domains show that applying Adapt to the BOCE recognizer can improve its performance by a factor of two to three.