Learning situation models in a smart home.
This paper addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart environment is represented by a situation model describing environment, users, and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed, and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.
Brdiczka
Oliver
O
Palo Alto Research Center, Palo Alto, CA 94304, USA. oliver@parc.com
Crowley
James L
JL
Reignier
Patrick
P
D000465:Algorithms; D001185:Artificial Intelligence; D001519:Behavior / Q000502:physiology*; D003198:Computer Simulation; D004777:Environment; D006801:Humans; D008390:Markov Chains; D010363:Pattern Recognition, Automated / Q000379:methods*; D012380:Role; D012963:Sociometric Techniques; D014741:Video Recording