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A formal framework for the decentralised diagnosis of large scale discrete event systems and its application to telecommunication networks

dc.contributor.authorPencole, Yannick
dc.contributor.authorCordier, Marie-Odile
dc.date.accessioned2015-12-13T22:46:02Z
dc.date.issued2005
dc.date.updated2015-12-11T10:26:47Z
dc.description.abstractWe address the problem of diagnosing large discrete event systems. Given a flow of observations from the system, the goal is to explain these observations on-line by identifying and localising possible failures and their consequences across the system. Model-based diagnosis approaches deal with this problem but, apart very recent proposals, either they require the computation of a global model of the system which is not possible with large discrete event systems, or they cannot perform on-line diagnosis. The contribution of this paper is the description and the implementation of a formal framework for the on-line decentralised diagnosis of such systems, framework which is based on the "divide and conquer" principle and does not require the global model computation. This paper finally describes the use of this framework in the monitoring of a real telecommunication network.
dc.identifier.issn0004-3702
dc.identifier.urihttp://hdl.handle.net/1885/80075
dc.publisherElsevier
dc.sourceArtificial Intelligence
dc.subjectKeywords: Algorithms; Artificial intelligence; Computational complexity; Discrete time control systems; Large scale systems; Problem solving; Decentralized models; Discrete event systems; Distributed artificial intelligence; Fault propagation; Telecommunication net Decentralised model; Discrete event systems; Distributed artificial intelligence; Fault propagation; Model-based diagnosis; Telecommunication networks
dc.titleA formal framework for the decentralised diagnosis of large scale discrete event systems and its application to telecommunication networks
dc.typeJournal article
local.bibliographicCitation.issue1-2
local.bibliographicCitation.lastpage170
local.bibliographicCitation.startpage121
local.contributor.affiliationPencole, Yannick, College of Engineering and Computer Science, ANU
local.contributor.affiliationCordier, Marie-Odile, Universite de Rennes
local.contributor.authoruidPencole, Yannick, u4054161
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080199 - Artificial Intelligence and Image Processing not elsewhere classified
local.identifier.ariespublicationMigratedxPub8408
local.identifier.citationvolume164
local.identifier.doi10.1016/j.artint.2005.01.002
local.identifier.scopusID2-s2.0-16244391819
local.type.statusPublished Version

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