Extracting Minimal Unsatisfiable Cores from Constraint Networks
Julien Vion,
Université d’Artois –
Abstract:
The problem of extracting Minimal Unsatisfiable Cores (MUCs) from constraint networks is a computationally hard problem which has a practical interest in many application domains such as configuration, planning, diagnosis, etc. Indeed, identifying one or several disjoint MUCs can help circumscribe different sources of inconsistency in order to repair a system. We propose an original approach that involves performing successive runs of a complete backtracking search, using constraint weighting, in order to surround an inconsistent part of a network, before identifying all transition constraints belonging to a MUC using a dichotomic process.
Date: 2006-Apr-11 Time: 14:30:00 Room: 217
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Keynote speakers:
David Posada, University of Vigo (class)
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Nadia El-Mabrouk, Université de Montréal (class)
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Ran Libeskind-Hadas, Claremont McKenna College (class and hands-on)
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The 13th Lisbon Machine Learning School | LxMLS 2023
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