A formal theory of plan recognition
A formal theory of plan recognition
The berkeley UNIX consultant project
Computational Linguistics
UM Translog: a planning domain for the development and benchmarking of planning systems
UM Translog: a planning domain for the development and benchmarking of planning systems
Fast planning through planning graph analysis
Artificial Intelligence
Acquisition of abstract plan descriptions for plan recognition
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Solution Techniques for Constraint Satisfaction Problems: Advanced Approaches
Artificial Intelligence Review
Dynamic Flexible Constraint Satisfaction
Applied Intelligence
Bayesian Models for Keyhole Plan Recognition in an Adventure Game
User Modeling and User-Adapted Interaction
Graph Construction and Analysis as a Paradigm for Plan Recognition
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Scalable and adaptive goal recognition
Scalable and adaptive goal recognition
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Goal recognition through goal graph analysis
Journal of Artificial Intelligence Research
Corpus-based, statistical goal recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Getting serious about parsing plans: a grammatical analysis of plan recognition
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
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Instead of using a plan library, the recognizer introduced in this paper uses a compact structure called flexible to represent goals, actions and states of the world. This method doesn't suffer the problem of acquisition and hand-coding a larger plan library as traditional methods do. The recognizer also extends classical methods in two directions. First, using flexible goals and actions via fuzzy sets, the recognizer can recognize goals even when the agent has not enough domain knowledge. Second, the recognizer offers a method for assessment of various plan hypothesis and eventual selection good ones. Since the recognizer is domain independent the method can be adapted in almost every domain. Empirical and theoretical results also show the method is efficiency and scalability.