Using modelica programs for deriving propositional horn clause abduction problems

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

Abstract

Despite ample advantages of model-based diagnosis, in practice its use has been somehow limited to proof-of-concept prototypes. Some reasons behind this observation are that the required modeling step is resource consuming, and also that this step requires additional training. In order to overcome these problems, we suggest to use modeling languages like Modelica that are already established in academia and industry for describing cyber-physical systems as basis for deriving logic based models. Together with observations about the modeled system, those models can then be used by an abductive diagnosis engine for deriving the root causes for detected defects. The idea behind our approach is to introduce fault models for the components written in Modelica, and to use the available simulation environment to determine behavioral deviations to the expected outcome of a fault free model. The introduced fault models and gained information about the resulting deviations can be directly mapped to horn clauses to be used for diagnosis.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - 39th Annual German Conference on AI, KI 2016, Proceedings
PublisherSpringer-Verlag Italia
Pages185-191
Number of pages7
Volume9904 LNAI
ISBN (Print)9783319460727
DOIs
Publication statusPublished - 2016
Event39th German Conference on Artificial Intelligence, KI 2016 - Klagenfurt, Austria
Duration: 26 Sep 201630 Sep 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9904 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference39th German Conference on Artificial Intelligence, KI 2016
CountryAustria
CityKlagenfurt
Period26/09/1630/09/16

    Fingerprint

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fields of Expertise

  • Information, Communication & Computing

Cite this

Peischl, B., Pill, I., & Wotawa, F. (2016). Using modelica programs for deriving propositional horn clause abduction problems. In Advances in Artificial Intelligence - 39th Annual German Conference on AI, KI 2016, Proceedings (Vol. 9904 LNAI, pp. 185-191). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9904 LNAI). Springer-Verlag Italia. https://doi.org/10.1007/978-3-319-46073-4_18