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Learning Situation Models for Providing Context-Aware Services

  • O. Brdiczka
  • J. L. Crowley
  • P. Reignier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4555)

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.

Keywords

Hide Markov Model Recognition Rate Sensor Perception Situation Model Learner Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • O. Brdiczka
    • 1
  • J. L. Crowley
    • 1
  • P. Reignier
    • 1
  1. 1.Project PRIMA, INRIA Rhône-Alpes, MontbonnotFrance

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