Abstract
The context paradigm emerges from different areas of Artificial Intelligence (AI). However, while significative formalizations have been proposed, contexts are either mapped on independent micro-theories or considered as different concurrent viewpoints with mappings between contexts to export/import knowledge. These logical formalisms focus on the semantic level and do not take into account dynamic low-level information such as those available from sensors via physical variables. This information is a key element of contexts in pervasive computing environments. In this paper, we introduce a formal framework where the knowledge representation of context bridges the gap between semantic high-level and low-level knowledge. The logical reasoning based on intuitionistic type theory and the Curry-Howard isomorphism is able to incorporate expert knowledge as well as technical resources such as computing variable properties.
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Dapoigny, R., Barlatier, P. (2007). Dependent Record Types for Dynamic Context Representation. In: Bramer, M., Coenen, F., Tuson, A. (eds) Research and Development in Intelligent Systems XXIII. SGAI 2006. Springer, London. https://doi.org/10.1007/978-1-84628-663-6_15
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DOI: https://doi.org/10.1007/978-1-84628-663-6_15
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