Learned Constraint Ordering for Consistency Based Direct Diagnosis

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

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.
Originalspracheenglisch
TitelAdvances and Trends in Artificial Intelligence
UntertitelFrom Theory to Practice. IEA/AIE 2019
ErscheinungsortCham
Herausgeber (Verlag)Springer
Seiten347-359
ISBN (Print)978-3-030-22998-6
DOIs
PublikationsstatusVeröffentlicht - 2019
Veranstaltung32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems - Graz, Österreich
Dauer: 9 Jul 201911 Jul 2019

Publikationsreihe

NameLecture Notes in Computer Science
Band11606

Konferenz

Konferenz32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems
KurztitelIEA/AIE 2019
LandÖsterreich
OrtGraz
Zeitraum9/07/1911/07/19

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