A formal theory of plan recognition
A formal theory of plan recognition
An efficient context-free parsing algorithm
Communications of the ACM
Learning Significant Locations and Predicting User Movement with GPS
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
Probabilistic grammars for plan recognition
Probabilistic grammars for plan recognition
Learning and inferring transportation routines
Artificial Intelligence
Dynamics-aware similarity of moving objects trajectories
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Spatially Constrained Grammars for Mobile Intention Recognition
Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Representing the meaning of spatial behavior by spatially grounded intentional systems
GeoS'05 Proceedings of the First international conference on GeoSpatial Semantics
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Mobile intention recognition is the problem of inferring a mobile user's intentions from her behavior in geographic space. Such behavior is constrained in space and time. Current approaches, however, have difficulties to handle temporal constraints. We therefore propose using the framework of time geography to formalize and visualize both spatial and temporal constraints for the mobile intention recognition problem. A new rule language is introduced which allows for modeling intentions with spatial and temporal constraints. A location-based game application demonstrates that interpreting a user's spatio-temporal behavior sequence in terms of intentions reduces ambiguity compared to mobile intention recognition without temporal constraints.