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An efficient diagnosis algorithm for inconsistent constraint sets

Published online by Cambridge University Press:  10 June 2011

A. Felfernig ,
M. Schubert  and
C. Zehentner
A. Felfernig*
Affiliation:
Institute for Software Technology, Graz University of Technology, Graz, Austria
M. Schubert
Affiliation:
Institute for Software Technology, Graz University of Technology, Graz, Austria
C. Zehentner
Affiliation:
Institute for Software Technology, Graz University of Technology, Graz, Austria
*
Reprint requests to: A. Felfernig, Institute for Software Technology, Graz University of Technology, Inffeldgasse 16b, A-8010 Graz, Austria. E-mail: [email protected]
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Abstract

Constraint sets can become inconsistent in different contexts. For example, during a configuration session the set of customer requirements can become inconsistent with the configuration knowledge base. Another example is the engineering phase of a configuration knowledge base where the underlying constraints can become inconsistent with a set of test cases. In such situations we are in the need of techniques that support the identification of minimal sets of faulty constraints that have to be deleted in order to restore consistency. In this paper we introduce a divide and conquer-based diagnosis algorithm (FastDiag) that identifies minimal sets of faulty constraints in an overconstrained problem. This algorithm is specifically applicable in scenarios where the efficient identification of leading (preferred) diagnoses is crucial. We compare the performance of FastDiag with the conflict-directed calculation of hitting sets and present an in-depth performance analysis that shows the advantages of our approach.

Keywords

Inconsistent Constraint SetsInteractive ConfigurationModel-Based DiagnosisPreferred Diagnosis
Type
Articles
Information
AI EDAM , Volume 26 , Issue 1 , February 2012 , pp. 53 - 62
Copyright
Copyright © Cambridge University Press 2012

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