Characterizing diagnoses and systems

Most approaches to model-based diagnosis describe a diagnosis for a system as a set of failing components that explains the symptoms. In order to characterize the typically very large number of diagnoses, usually only the minimal such sets of failing components are represented. This method of charac...

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Bibliographic Details
Published inArtificial intelligence Vol. 56; no. 2; pp. 197 - 222
Main Authors de Kleer, Johan, Mackworth, Alan K., Reiter, Raymond
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.08.1992
Elsevier
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Learning and adaptive systems
Conflict
Reasoning
Expert system
Prime implicant
Failure detection
Diagnosis
Computer system
Artificial intelligence
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Summary:Most approaches to model-based diagnosis describe a diagnosis for a system as a set of failing components that explains the symptoms. In order to characterize the typically very large number of diagnoses, usually only the minimal such sets of failing components are represented. This method of characterizing all diagnoses is inadequate in general, in part because not every superset of the faulty components of a diagnosis necessarily provides a diagnosis. In this paper we analyze the concept of diagnosis in depth exploiting the notions of implicate/implicant and prime implicate/implicant. We use these notions to consider two alternative approaches for addressing the inadequacy of the concept of minimal diagnosis. First, we propose a new concept, that of kernel diagnosis, which is free of this problem with minimal diagnosis. This concept is useful to both the consistency and abductive views of diagnosis. Second, we consider restricting the axioms used to describe the system to ensure that the concept of minimal diagnosis is adequate.
ISSN:0004-3702
1872-7921
DOI:10.1016/0004-3702(92)90027-U