Learning Situation Models for Providing Context-Aware Services

Oliver Brdiczka 1, 2 James L. Crowley 2 Patrick Reignier 2
2 PRIMA - Perception, recognition and integration for observation of activity
Inria Grenoble - Rhône-Alpes, UJF - Université Joseph Fourier - Grenoble 1, INPG - Institut National Polytechnique de Grenoble , CNRS - Centre National de la Recherche Scientifique : UMR5217
Abstract : In order to provide information and communication services without disrupting human activity, information services must implicitly conform to the current context of human activity. However, the variability of human environments and human preferences make it impossible to preprogram the appropriate behaviors for a context aware service. One approach to overcoming this obstacle is to have services adapt behavior to individual preferences though feedback from users. This article describes a method for learning situation models to drive context-aware services. With this approach an initial simplified situation model is adapted to accommodate user preferences by a supervised learning algorithm using feedback from users. To bootstrap this process, the initial situation model is acquired by applying an automatic segmentation process to sample observation of human activities. This model is subsequently adapted to different operating environments and human preferences through interaction with users, using a supervised learning algorithm.
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Oliver Brdiczka, James L. Crowley, Patrick Reignier. Learning Situation Models for Providing Context-Aware Services. the 12th International Conference on Human-Computer Interaction, Jul 2007, Beijing, China. ⟨inria-00352944⟩

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