Model-based diagnosis with multiple observations

Alexey Ignatiev, António Morgado, Georg Weissenbacher, João Marques-Silva

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

9 Citations (Scopus)


Existing automated testing frameworks require multiple observations to be jointly diagnosed with the purpose of identifying common fault locations. This is the case for example with continuous integration tools. This paper shows that existing solutions fail to compute the set of minimal diagnoses, and as a result run times can increase by orders of magnitude. The paper proposes not only solutions to correct existing algorithms, but also conditions for improving their run times. Nevertheless, the diagnosis of multiple observations raises a number of important computational challenges, which even the corrected algorithms are often unable to cope with. As a result, the paper devises a novel algorithm for diagnosing multiple observations, which is shown to enable significant performance improvements in practice.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
EditorsSarit Kraus
Place of PublicationMarina del Rey CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages8
ISBN (Electronic)9780999241141
Publication statusPublished - 2019
Externally publishedYes
EventInternational Joint Conference on Artificial Intelligence 2019 - Macao, China
Duration: 10 Aug 201916 Aug 2019
Conference number: 28th (Proceedings)


ConferenceInternational Joint Conference on Artificial Intelligence 2019
Abbreviated titleIJCAI 2019
Internet address


  • Constraints and SAT
  • Knowledge Representation and Reasoning
  • Diagnosis and Abductive Reasoning
  • Heuristic Search and Game Playing
  • Combinatorial Search and Optimisation
  • SAT: Applications
  • MaxSAT, MinSAT

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