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Documents authored by Kukla, Karol

Artifact
Software
DOI: 10.4230/artifacts.24965
jadlownik/FaultDiagnosis

Authors: Karol Kukla

Abstract
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Karol Kukla. jadlownik/FaultDiagnosis (Software, Source code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@misc{dagstuhl-artifact-24965,
   title = {{jadlownik/FaultDiagnosis}}, 
   author = {Kukla, Karol},
   note = {Software, swhId: \href{https://archive.softwareheritage.org/swh:1:dir:b8a40cc86347d60c8efb7ce10df9343400eee88e;origin=https://github.com/jadlownik/FaultDiagnosis;visit=swh:1:snp:f3ecc9d71018391ddad295112400f0de0a31daf7;anchor=swh:1:rev:ad6e2ff065689d228955580d57ace4f4b72a1495}{\texttt{swh:1:dir:b8a40cc86347d60c8efb7ce10df9343400eee88e}} (visited on 2025-11-10)},
   url = {https://github.com/jadlownik/FaultDiagnosis/tree/develop},
   doi = {10.4230/artifacts.24965},
}
Document
DOI: 10.4230/OASIcs.DX.2025.2
Are Diagnostic Concepts Within the Reach of LLMs?

Authors: Anna Sztyber-Betley, Elodie Chanthery, Louise Travé-Massuyès, Silke Merkelbach, Karol Kukla, Maxence Glotin, Alexander Diedrich, and Oliver Niggemann

Published in: OASIcs, Volume 136, 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)

Abstract
Model-based diagnosis is a cornerstone of system health monitoring, allowing for the identification of faulty components based on observed behavior and a formal system model. However, obtaining a useful and reliable model is often an expensive and manual task. While the generation of a formal model was the aim of previous work, in this paper, we propose a methodology to use large language models to generate Minimal Structurally Overdetermined sets (MSOs). MSOs are specific subsets of the model equations from which diagnosis tests can be obtained. We investigate two different directions: (i) the large-language-models' ability to generate MSO sets for hybrid systems, similar to those generated by the well-known Fault Diagnosis Toolbox (FDT) (ii) the automated generation of MSOs for Boolean circuits, as well as the computation of the diagnoses. We thus show how both dynamic and static systems can be analysed by large-language models and how their output can be used for effective fault diagnosis. We evaluate our approach on a set of arithmetic and logic circuits, using OpenAI’s LLMs 4o-mini, o1, and o3-mini.
Cite as

Anna Sztyber-Betley, Elodie Chanthery, Louise Travé-Massuyès, Silke Merkelbach, Karol Kukla, Maxence Glotin, Alexander Diedrich, and Oliver Niggemann. Are Diagnostic Concepts Within the Reach of LLMs?. In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 2:1-2:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{sztyberbetley_et_al:OASIcs.DX.2025.2,
  author =	{Sztyber-Betley, Anna and Chanthery, Elodie and Trav\'{e}-Massuy\`{e}s, Louise and Merkelbach, Silke and Kukla, Karol and Glotin, Maxence and Diedrich, Alexander and Niggemann, Oliver},
  title =	{{Are Diagnostic Concepts Within the Reach of LLMs?}},
  booktitle =	{36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025)},
  pages =	{2:1--2:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-394-2},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{136},
  editor =	{Quinones-Grueiro, Marcos and Biswas, Gautam and Pill, Ingo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2025.2},
  URN =		{urn:nbn:de:0030-drops-247913},
  doi =		{10.4230/OASIcs.DX.2025.2},
  annote =	{Keywords: Fault Diagnosis, Large Language Models, LLMs, Model Based Diagnosis, MSO, Redundancy Relations, Conflicts, Diagnoses}
}
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