The Architecture of Cognition
Theory of Modeling and Simulation
Theory of Modeling and Simulation
Modelling and Using Imperfect Context Information
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
Combining micro and macro-modeling in DEVS for computational biology
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
A general model for online probabilistic plan recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Multi-agent plan recognition with partial team traces and plan libraries
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Goal recognition over POMDPs: inferring the intention of a POMDP agent
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Accommodating human variability in human-robot teams through theory of mind
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Towards creating assistive software by employing human behavior models
Journal of Ambient Intelligence and Smart Environments - A software engineering perspective on smart applications for AmI
Modeling agents and their environment in multi-level-DEVS
Proceedings of the Winter Simulation Conference
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Activity recognition is a challenging research problem in ubiquitous computing domain and has to tackle omnipresent uncertainties, e.g., resulting from ambiguous or intermittent sensor readings. In this paper, we introduce an activity recognition approach based on causal modeling and probabilistic plan recognition. To evaluate the performance of our approach systematically, we generated sensor data with different error rates using a simulation. This data served as input for the activity recognition in a series of experiments. In these experiments we stepwise introduced and combined additional sources of uncertainty, i.e., different duration models and ignoring certain sensors, to demonstrate the robustness of our approach. Our evaluation shows that Computational Causal Behavior Models provide a basis for a robust activity recognition system.