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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Learned Constraint Ordering for Consistency Based Direct Diagnosis
verfasst von : Seda Polat Erdeniz, Alexander Felfernig, Muesluem Atas
Erschienen in: Advances and Trends in Artificial Intelligence. From Theory to Practice
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Abstract
Configuration systems must be able to deal with inconsistencies which can occur in different contexts. Especially in interactive settings, where users specify requirements and a constraint solver has to identify solutions, inconsistencies may more often arise. In inconsistency situations, there is a need of diagnosis methods that support the identification of minimal sets of constraints that have to be adapted or deleted in order to restore consistency. A diagnosis algorithm’s performance can be evaluated in terms of time to find a diagnosis (runtime) and diagnosis quality. Runtime efficiency of diagnosis is especially crucial in real-time scenarios such as production scheduling, robot control, and communication networks. However, there is a trade off between diagnosis quality and the runtime efficiency of diagnostic reasoning. In this paper, we deal with solving the quality-runtime performance trade off problem of direct diagnosis. In this context, we propose a novel learning approach for constraint ordering in direct diagnosis. We show that our approach improves the runtime performance and diagnosis quality at the same time.