Multi-agent Architecture for Heterogeneous Reasoning under Uncertainty Combining MSBN and Ontologies in Distributed Network Diagnosis

Carrera Barroso, Álvaro ORCID: https://orcid.org/0000-0002-0319-036X and Iglesias Fernández, Carlos Ángel ORCID: https://orcid.org/0000-0002-1755-2712 (2011). Multi-agent Architecture for Heterogeneous Reasoning under Uncertainty Combining MSBN and Ontologies in Distributed Network Diagnosis. In: "2011 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT),", 22/08/2011 - 27/08/2011, Lyon, Francia. ISBN 978-1-4577-1373-6.

Description

Title: Multi-agent Architecture for Heterogeneous Reasoning under Uncertainty Combining MSBN and Ontologies in Distributed Network Diagnosis
Author/s:
Item Type: Presentation at Congress or Conference (Article)
Event Title: 2011 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT),
Event Dates: 22/08/2011 - 27/08/2011
Event Location: Lyon, Francia
Title of Book: Proceedings of 2011 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT),
Date: 2011
ISBN: 978-1-4577-1373-6
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería de Sistemas Telemáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

This article proposes a MAS architecture for network diagnosis under uncertainty. Network diagnosis is divided into two inference processes: hypothesis generation and hypothesis confirmation. The first process is distributed among several agents based on a MSBN, while the second one is carried out by agents using semantic reasoning. A diagnosis ontology has been defined in order to combine both inference processes. To drive the deliberation process, dynamic data about the influence of observations are taken during diagnosis process. In order to achieve quick and reliable diagnoses, this influence is used to choose the best action to perform. This approach has been evaluated in a P2P video streaming scenario. Computational and time improvements are highlight as conclusions.

More information

Item ID: 12212
DC Identifier: https://oa.upm.es/12212/
OAI Identifier: oai:oa.upm.es:12212
Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumb...
Deposited by: Memoria Investigacion
Deposited on: 29 Aug 2012 08:17
Last Modified: 01 Apr 2023 09:48
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