Defeasible contextual reasoning with arguments in ambient intelligence

Antonis Bikakis, Grigoris Antoniou

Research output: Contribution to journalArticle

49 Citations (Scopus)

Abstract

The imperfect nature of context in Ambient Intelligence environments and the special characteristics of the entities that possess and share the available context information render contextual reasoning a very challenging task. The accomplishment of this task requires formal models that handle the involved entities as autonomous logic-based agents and provide methods for handling the imperfect and distributed nature of context. This paper proposes a solution based on the Multi-Context Systems paradigm in which local context knowledge of ambient agents is encoded in rule theories (contexts), and information flow between agents is achieved through mapping rules that associate concepts used by different contexts. To handle imperfect context, we extend Multi-Context Systems with nonmonotonic features: local defeasible theories, defeasible mapping rules, and a preference ordering on the system contexts. On top of this model, we have developed an argumentation framework that exploits context and preference information to resolve potential conflicts caused by the interaction of ambient agents through the mappings, and a distributed algorithm for query evaluation.

LanguageEnglish
Article number5416723
Pages1492-1506
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume22
Issue number11
DOIs
Publication statusPublished - 1 Oct 2010
Externally publishedYes

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Parallel algorithms
Ambient intelligence

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Defeasible contextual reasoning with arguments in ambient intelligence. / Bikakis, Antonis; Antoniou, Grigoris.

In: IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 11, 5416723, 01.10.2010, p. 1492-1506.

Research output: Contribution to journalArticle

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