Learned Constraint Ordering for Consistency Based Direct Diagnosis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationAdvances and Trends in Artificial Intelligence
Subtitle of host publicationFrom Theory to Practice. IEA/AIE 2019
Place of PublicationCham
PublisherSpringer
Pages347-359
ISBN (Print)978-3-030-22998-6
DOIs
Publication statusPublished - 2019
Event32nd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems - Graz, Austria
Duration: 9 Jul 201911 Jul 2019

Publication series

NameLecture Notes in Computer Science
Volume11606

Conference

Conference32nd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems
Abbreviated titleIEA/AIE 2019
CountryAustria
CityGraz
Period9/07/1911/07/19

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