Abstract
Context-aware systems make use of contextual information to adapt their functionality to current environment state, or user needs and habits. One of the major problems concerning them is the fact, that there is no warranty that the contextual information will be available, nor certain at the time when the reasoning should be performed. This may be due to measurement errors, sensor inaccuracy, or semantic ambiguities of modeled concepts. Several approaches were developed to solve uncertainty in context knowledge bases, including probabilistic reasoning, fuzzy logic, or certainty factors. However, handling uncertainties in highly dynamic, mobile environments still requires more consideration. In this paper we perform comparison of application of different uncertainty modeling approaches to mobile context-aware environments. We also present an exemplary solution based on modified certainty factors algebra and logic-based knowledge representation for solving uncertainties caused by the imprecision of context-providers.
The paper is supported by the AGH UST Grant.
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Bobek, S., Nalepa, G.J. (2014). Incomplete and Uncertain Data Handling in Context-Aware Rule-Based Systems with Modified Certainty Factors Algebra. In: Bikakis, A., Fodor, P., Roman, D. (eds) Rules on the Web. From Theory to Applications. RuleML 2014. Lecture Notes in Computer Science, vol 8620. Springer, Cham. https://doi.org/10.1007/978-3-319-09870-8_11
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DOI: https://doi.org/10.1007/978-3-319-09870-8_11
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