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Decentralized Reasoning in Ambient Intelligence [electronic resource] /by José Viterbo, Markus Endler.

by Viterbo, José [author.]; Endler, Markus [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computer Science: Publisher: London : Springer London : 2012.Description: IX, 96 p. 25 illus. online resource.ISBN: 9781447141686.Subject(s): Computer science | Computer Communication Networks | Artificial intelligence | Computer Science | Computer Communication Networks | Artificial Intelligence (incl. Robotics)DDC classification: 004.6 Online resources: Click here to access online
Contents:
Introduction -- Fundamental Concepts -- Related Work -- Cooperative Reasoning -- Our Approach for Cooperative Reasoning -- Case Study -- Implementation -- Evaluation -- Conclusion.
In: Springer eBooksSummary: In Ambient Intelligence (AmI) systems, reasoning is fundamental for triggering actions or adaptations according to specific situations that may be meaningful and relevant to some applications. However, such reasoning operations may need to evaluate context data collected from distributed sources and stored in different devices, as usually not all context data is readily available to the reasoners within the system. Decentralized Reasoning in Ambient Intelligence proposes a decentralized reasoning approach for performing rule-based reasoning about context data targeting AmI systems. For this purpose, the authors define a context model assuming context data distributed over two sides: the user side, represented by the users and their mobile devices, and the ambient side, represented by the fixed computational infrastructure and ambient services. They formalize the cooperative reasoning operation — in which two entities cooperate to perform decentralized rule-based reasoning — and define a complete process to perform this operation.
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Introduction -- Fundamental Concepts -- Related Work -- Cooperative Reasoning -- Our Approach for Cooperative Reasoning -- Case Study -- Implementation -- Evaluation -- Conclusion.

In Ambient Intelligence (AmI) systems, reasoning is fundamental for triggering actions or adaptations according to specific situations that may be meaningful and relevant to some applications. However, such reasoning operations may need to evaluate context data collected from distributed sources and stored in different devices, as usually not all context data is readily available to the reasoners within the system. Decentralized Reasoning in Ambient Intelligence proposes a decentralized reasoning approach for performing rule-based reasoning about context data targeting AmI systems. For this purpose, the authors define a context model assuming context data distributed over two sides: the user side, represented by the users and their mobile devices, and the ambient side, represented by the fixed computational infrastructure and ambient services. They formalize the cooperative reasoning operation — in which two entities cooperate to perform decentralized rule-based reasoning — and define a complete process to perform this operation.

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