Strategies for contextual reasoning with conflicts in ambient intelligence

Ambient Intelligence environments host various agents that collect, process, change and share the available context information. The imperfect nature of context, the open and dynamic nature of such environments and the special characteristics of ambient agents have introduced new research challenges...

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Bibliographic Details
Published in:Knowledge and information systems 2011-04, Vol.27 (1), p.45-84
Main Authors: Bikakis, Antonis, Antoniou, Grigoris, Hasapis, Panayiotis
Format: Article
Language:English
Subjects:
Agents (artificial intelligence)
Algorithms
Ambient intelligence
Applied sciences
Artificial intelligence
Cellular telephones
Classrooms
Computer Science
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Data Mining and Knowledge Discovery
Database Management
Dynamical systems
Dynamics
Exact sciences and technology
Information Storage and Retrieval
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
Intelligent systems
IT in Business
Knowledge
Mapping
Order disorder
Preferences
Regular Paper
Software
Strategy
Studies
Wireless communications
Wireless networks
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Summary:Ambient Intelligence environments host various agents that collect, process, change and share the available context information. The imperfect nature of context, the open and dynamic nature of such environments and the special characteristics of ambient agents have introduced new research challenges in the study of Distributed Artificial Intelligence. This paper proposes a solution based on the Multi-Context Systems paradigm, according to which local 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 resolve potential inconsistencies that may arise from the interaction of contexts through their mappings ( global conflicts ), we use a preference ordering on the system contexts, which may express the confidence that an agent has in the knowledge imported by other agents. On top of this model, we have developed four alternative strategies for global conflicts resolution, which mainly differ in the type and extent of context and preference information that is used to resolve potential conflicts. The four strategies have been respectively implemented in four versions of a distributed algorithm for query evaluation and evaluated in a simulated P2P system.
ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-010-0293-0