Using modelica programs for deriving propositional horn clause abduction problems

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

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.

Originalspracheenglisch
TitelAdvances in Artificial Intelligence - 39th Annual German Conference on AI, KI 2016, Proceedings
Herausgeber (Verlag)Springer-Verlag Italia
Seiten185-191
Seitenumfang7
Band9904 LNAI
ISBN (Print)9783319460727
DOIs
PublikationsstatusVeröffentlicht - 2016
Veranstaltung39th German Conference on Artificial Intelligence, KI 2016 - Klagenfurt, Österreich
Dauer: 26 Sep 201630 Sep 2016

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band9904 LNAI
ISSN (Print)03029743
ISSN (elektronisch)16113349

Konferenz

Konferenz39th German Conference on Artificial Intelligence, KI 2016
LandÖsterreich
OrtKlagenfurt
Zeitraum26/09/1630/09/16

Fingerprint

Modelica
Horn clause
Abduction
Fault
Deviation
Model-based Diagnosis
Model
Simulation Environment
Modeling Language
Engine
Defects
Roots
Prototype
Industry
Logic
Resources
Engines
Modeling

ASJC Scopus subject areas

  • !!Theoretical Computer Science
  • !!Computer Science(all)

Fields of Expertise

  • Information, Communication & Computing

Dies zitieren

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 (Band 9904 LNAI, S. 185-191). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9904 LNAI). Springer-Verlag Italia. https://doi.org/10.1007/978-3-319-46073-4_18

Using modelica programs for deriving propositional horn clause abduction problems. / Peischl, Bernhard; Pill, Ingo ; Wotawa, Franz.

Advances in Artificial Intelligence - 39th Annual German Conference on AI, KI 2016, Proceedings. Band 9904 LNAI Springer-Verlag Italia, 2016. S. 185-191 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9904 LNAI).

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

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. Bd. 9904 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 9904 LNAI, Springer-Verlag Italia, S. 185-191, Klagenfurt, Österreich, 26/09/16. https://doi.org/10.1007/978-3-319-46073-4_18
Peischl B, Pill I, Wotawa F. Using modelica programs for deriving propositional horn clause abduction problems. in Advances in Artificial Intelligence - 39th Annual German Conference on AI, KI 2016, Proceedings. Band 9904 LNAI. Springer-Verlag Italia. 2016. S. 185-191. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-46073-4_18
Peischl, Bernhard ; Pill, Ingo ; Wotawa, Franz. / Using modelica programs for deriving propositional horn clause abduction problems. Advances in Artificial Intelligence - 39th Annual German Conference on AI, KI 2016, Proceedings. Band 9904 LNAI Springer-Verlag Italia, 2016. S. 185-191 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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