Abstract
This paper presents a twofold context modelling approach that integrates beliefs (uncertain knowledge) and facts to reason about various everyday situations. Awareness of everyday situations enables mobile devices to adapt to the social and conceptual settings in which they operate; it also enables resources which share a similar context to cooperate in order to carry out a distributed task on behalf of their user. Our context modelling process involves the identification of the context of interest, the determination of those aspects of a context which can be captured by employing sensors, the determination of contextual states for each aspect, and finally, the determination of logical and probabilistic relationships between the contextual aspects and the context they represent. We demonstrate our approach by modelling physical places. Data from various heterogeneous sensors build our system’s belief, while containment relationships build its factual knowledge regarding places. The system utilises its belief and factual knowledge to reason about the whereabouts of a mobile user.
Keywords
- Bayesian Network
- Linguistic Variable
- Context Modelling
- Pervasive Computing
- Factual Knowledge
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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Dargie, W., Springer, T. (2007). Integrating Facts and Beliefs to Model and Reason About Context. In: Indulska, J., Raymond, K. (eds) Distributed Applications and Interoperable Systems. DAIS 2007. Lecture Notes in Computer Science, vol 4531. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72883-2_2
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DOI: https://doi.org/10.1007/978-3-540-72883-2_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72881-8
Online ISBN: 978-3-540-72883-2
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