Efficient Explanations for Inconsistent Constraint Sets

Authors:
Alexander Felfernig;Monika Schubert;Monika Mandl;Gerhard Friedrich;Erich Teppan
Affiliations:
TU Graz, Austria, email: {felfernig, schubert, mandl}@ist.tugraz.at;TU Graz, Austria, email: {felfernig, schubert, mandl}@ist.tugraz.at;TU Graz, Austria, email: {felfernig, schubert, mandl}@ist.tugraz.at;University of Klagenfurt, Austria, email: {friedrich, teppan}@uni-klu.ac.at;University of Klagenfurt, Austria, email: {friedrich, teppan}@uni-klu.ac.at
Venue:
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Year:
2010

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Abstract

Constraint sets can become inconsistent in different contexts. We are interested in identifying minimal sets of constraints that have to be adapted or deleted in order to restore consistency. In this paper we sketch a highly efficient divide-and-conquer based diagnosis approach which identifies minimal sets of faulty constraints in a given over-constrained problem. This approach is specifically applicable in scenarios where the efficient identification of leading (preferred) diagnoses is crucial.