Cooperating with people: the intelligent classroom
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Plan-based interfaces: keeping track of user tasks and acting to cooperate
Proceedings of the 7th international conference on Intelligent user interfaces
System Software for Ubiquitous Computing
IEEE Pervasive Computing
Project Aura: Toward Distraction-Free Pervasive Computing
IEEE Pervasive Computing
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Modeling Uncertainty in Context-Aware Computing
Proceedings of the Fourth Annual ACIS International Conference on Computer and Information Science
Cognitive Technical Systems -- What Is the Role of Artificial Intelligence?
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Effects of agendas on model-based intention inference of cooperative teams
COLCOM '07 Proceedings of the 2007 International Conference on Collaborative Computing: Networking, Applications and Worksharing
A general model for online probabilistic plan recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Towards an integrated approach for task modeling and human behavior recognition
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: interaction design and usability
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Many context-aware projects try to develop the next step in human computer interaction, systems that adapt to a users need and help him to focus on his specific task. Probabilistic models are used to infer the current activity of a user. These techniques for predicting the actions of a user are often custom-tailored to a fixed location and scenario. We developed a method to generate probabilistic models for different context, therefore broaden their use in different domains of ubiquitous computing. This makes our intention analysis more generic.