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Diagnosability testing with satisfiability algorithms

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dc.contributor.authorRintanen, Jussi
dc.contributor.authorGrastien, Alban
dc.contributor.editorVeloso, M. M.
dc.coverage.spatialHyderabad India
dc.date.accessioned2015-12-08T22:11:00Z
dc.date.createdJanuary 6-12 2007
dc.date.issued2007
dc.date.updated2022-08-07T08:19:54Z
dc.description.abstractWe show how testing whether a system is diagnosable can be reduced to the satisfiability problem and how satisfiability algorithms yield a very efficient approach to testing diagnosability. Diagnosability is the question whether it is always possible to k
dc.identifier.urihttp://hdl.handle.net/1885/29605
dc.publisherCarnegie Mellon University
dc.relation.ispartofseriesInternational Joint Conference on Artificial Intelligence (IJCAI 2007)
dc.sourceProceedings of the International Joint Conference on Artificial Intelligence
dc.source.urihttp://www.ijcai.org/proceedings07.php
dc.subjectKeywords: AI planning; Diagnosability; Failure behaviors; Finding paths; Satisfiability algorithms; Satisfiability problems; System behaviors; Transition graphs; Algorithms; Artificial intelligence; Formal logic
dc.titleDiagnosability testing with satisfiability algorithms
dc.typeConference paper
local.bibliographicCitation.lastpage537
local.bibliographicCitation.startpage532
local.contributor.affiliationRintanen, Jussi , College of Engineering and Computer Science, ANU
local.contributor.affiliationGrastien, Alban , College of Engineering and Computer Science, ANU
local.contributor.authoruidRintanen, Jussi , u1814932
local.contributor.authoruidGrastien, Alban , a230022
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.ariespublicationu8803936xPUB66
local.identifier.scopusID2-s2.0-84880912141
local.type.statusPublished Version

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