Applying matrix factorization to consistency-based direct diagnosis

Seda Polat Erdeniz*, Alexander Felfernig, Müslüm Atas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 article, we deal with solving the quality-runtime performance trade off problem of direct diagnosis. In this context, we propose a novel learning approach based on matrix factorization for constraint ordering. We show that our approach improves runtime performance and diagnosis quality at the same time.
Original languageEnglish
Number of pages13
JournalApplied Intelligence
Publication statusE-pub ahead of print - 14 May 2021


  • Configuration systems
  • Constraint satisfaction
  • Diagnosis
  • Matrix factorization

ASJC Scopus subject areas

  • Artificial Intelligence


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