Abstract
Research in advanced context-aware systems has clearly shown a need to capture the inherent uncertainty in the physical world, especially in human behavior. Modelling approaches that employ the concept of probability, especially in combination with Bayesian methods, are promising candidates to solve the pending problems. This paper analyzes the requirements for such models in order to enable user-friendly, adaptive and especially scalable operation of context-aware systems. It is conjectured that a successful system may not only use Bayesian techniques to infer probabilities from known probability tables but learn, i.e. estimate the probabilities in these tables by observing user behavior.
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Angermann, M., Robertson, P., Strang, T. (2005). Issues and Requirements for Bayesian Approaches in Context Aware Systems. In: Strang, T., Linnhoff-Popien, C. (eds) Location- and Context-Awareness. LoCA 2005. Lecture Notes in Computer Science, vol 3479. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11426646_22
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DOI: https://doi.org/10.1007/11426646_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25896-4
Online ISBN: 978-3-540-32042-5
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