How to dynamically merge Markov decision processes
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Learning to Detect User Activity and Availability from a Variety of Sensor Data
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
HHMM Based Recognition of Human Activity*This paper was presented at MVA2005.
IEICE - Transactions on Information and Systems
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Understanding human intentions via hidden markov models in autonomous mobile robots
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Decision making in assistive environments using multimodal observations
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Planning for human-robot interaction using time-state aggregated POMDPs
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
A decision-theoretic approach to task assistance for persons with dementia
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Dialogue management system is originated when human-computer interaction (HCI) was dominated by a single computer. With the development of sensor networks and pervasive techniques, the HCI has to adapt into pervasive environments. Pervasive interaction is a form of HCI derived under the context of pervasive computing. This paper introduces a pervasive interaction based planning and reasoning system for individuals with cognitive impairment, for their activities of daily living. Our system is a fusion of speech prompt, speech recognition as well as events from sensor networks. The system utilizes Markov decision processes for activity planning, and partially observable Markov decision processes for action planning and executing. Multimodal and multi-observation is the characteristics of a pervasive interaction system. Experimental results demonstrate the flexible effect the reminder system works for activity planning.