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

Carrera Barroso, Alvaro y Iglesias Fernandez, Carlos Angel (2011). Multi-agent Architecture for Heterogeneous Reasoning under Uncertainty Combining MSBN and Ontologies in Distributed Network Diagnosis. En: "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.

Descripción

Título: Multi-agent Architecture for Heterogeneous Reasoning under Uncertainty Combining MSBN and Ontologies in Distributed Network Diagnosis
Autor/es:
  • Carrera Barroso, Alvaro
  • Iglesias Fernandez, Carlos Angel
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 2011 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT),
Fechas del Evento: 22/08/2011 - 27/08/2011
Lugar del Evento: Lyon, Francia
Título del Libro: Proceedings of 2011 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT),
Fecha: 2011
ISBN: 978-1-4577-1373-6
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería de Sistemas Telemáticos [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 12212
Identificador DC: http://oa.upm.es/12212/
Identificador OAI: oai:oa.upm.es:12212
URL Oficial: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6040771&tag=1
Depositado por: Memoria Investigacion
Depositado el: 29 Ago 2012 08:17
Ultima Modificación: 21 Abr 2016 11:25
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