On the use of answer set programming for model-based diagnosis

Franz Wotawa*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Model-based diagnosis has been an active area of AI for several decades leading to many applications ranging from automotive to space. The underlying idea is to utilize a model of a system to localize faults in the system directly. Model-based diagnosis usually is implemented using theorem provers or constraint solvers combined with specialized diagnosis algorithms. In this paper, we contribute to research in model-based diagnosis and present a way of using answer set programming for computing diagnoses. In particular, we discuss a specific coding of diagnosis problems as answer set programs, and answer the research question whether answer set programming can be used for diagnosis in practice. For this purpose, we come up with an experimental study based on Boolean circuits comparing diagnosis using answer set programming with diagnosis based on a specialized diagnosis algorithm. Although, the specialized algorithm provide diagnoses in shorter time on average, answer set programming offers additional features making it very much attractive to be used in practice.

Original languageEnglish
Title of host publicationTrends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings
EditorsHamido Fujita, Jun Sasaki, Philippe Fournier-Viger, Moonis Ali
PublisherSpringer Science and Business Media Deutschland GmbH
Pages518-529
Number of pages12
ISBN (Print)9783030557881
DOIs
Publication statusPublished - 1 Jan 2020
Event33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems - Hybrider Event, Japan
Duration: 22 Sep 202025 Sep 2020

Publication series

NameLecture Notes in Computer Science
Volume12144
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems
Abbreviated titleIEA/AIE 2020
Country/TerritoryJapan
CityHybrider Event
Period22/09/2025/09/20

Keywords

  • Answer set programming
  • Experimental evaluation
  • Model-based diagnosis
  • Modeling for diagnosis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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