Applying matrix factorization to consistency-based direct diagnosis

Seda Polat Erdeniz*, Alexander Felfernig, Müslüm Atas

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikel

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 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.
Originalspracheenglisch
Seitenumfang13
FachzeitschriftApplied Intelligence
Frühes Online-Datum14 Mai 2021
DOIs
PublikationsstatusElektronische Veröffentlichung vor Drucklegung. - 14 Mai 2021

ASJC Scopus subject areas

  • Artificial intelligence

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