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Artificial Intelligence
Constraint propagation algorithms for temporal reasoning: a revised report
Readings in qualitative reasoning about physical systems
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DDL.1: a formal description of a constraint representation language for physical domains
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IEEE Intelligent Systems
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
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ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
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International Journal of Robotics Research
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Image and Vision Computing
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UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
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Robotics and Autonomous Systems
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UIC '09 Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing
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AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Journal of Ambient Intelligence and Smart Environments
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Journal of Ambient Intelligence and Smart Environments
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ACM Transactions on Intelligent Systems and Technology (TIST)
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PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
A Human Aware Mobile Robot Motion Planner
IEEE Transactions on Robotics
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
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In this article we address the problem of realizing a service-providing reasoning infrastructure for pro-active human assistance in intelligent environments. We propose SAM, an architecture which leverages temporal knowledge represented as relations in Allen's interval algebra and constraint-based temporal planning techniques. SAM provides two key capabilities for contextualized service provision: human activity recognition and planning for controlling pervasive actuation devices. While drawing inspiration from several state-of-the-art approaches, SAM provides a unique feature which has thus far not been addressed in the literature, namely the seamless integration of these two key capabilities. It does so by leveraging a constraint-based reasoning paradigm whereby both requirements for recognition and for planning/execution are represented as constraints and reasoned upon continuously. This work is partially supported by NovaMedTech project “Ängen” (EU regional funds) and by the Swedish Knowledge Foundation (KK Stiftelsen).