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Challenges for Model-Based Diagnosis

Authors Ingo Pill , Johan de Kleer

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Ingo Pill
  • Institute of Software Technology, Graz University of Technology, Austria
Johan de Kleer
  • c-infinity, Mountain View, CA, USA

Acknowledgements

We would like to thank the DX community for discussions on topics related to this paper. Among other people, this specifically includes (in a.o.) Gautam Biswas, Alessandro Cimatti, Alex Feldman, Kai Goebel, Meir Kalech, Ion Matei, Oliver Niggemann, Markus Stumptner, Louise Trave-Massuyes, Franz Wotawa, and Marina Zanella. This article was influenced also by discussions led at the Dagstuhl Seminar 24031 Fusing Causality, Reasoning, and Learning for Fault Management and Diagnosis, where we would like to thank the organizers as well as Schloss Dagstuhl Leinbniz-Zentrum für Informatik for organizing and hosting this seminar.

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Ingo Pill and Johan de Kleer. Challenges for Model-Based Diagnosis. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 6:1-6:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/OASIcs.DX.2024.6

Abstract

Since the seminal works by Reiter and de Kleer and Williams published in the late 80’s, Model-based Diagnosis has been a significant area of research. This has been motivated by the fact that MBD assists us in tackling a challenge that we face almost on a daily basis, i.e., by MBD allowing us to reason in a structured manner about the root causes for some encountered problem. MBD achieves this in an intuitive, complete and sound way, based on the central idea of investigating the compliance of some observed behavior with a model that describes how a system should behave - given this or that input scenario and parameter set. Over the last 40 years, MBD has been adopted for a multitude of applications, and we saw the emergence of a diverse set of algorithmic, optimizations, as well as extensions to the initial theoretical concepts.We argue that MBD remains highly relevant, with numerous scientific challenges to tackle as we face increasingly complex diagnostic problems. We discuss several such challenges and suggest related topics for PhD theses that have the potential to significantly contribute to the state-of-the-art in MBD research.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Causal reasoning and diagnostics
Keywords
  • Model-based Diagnosis
  • Diagnosis
  • Algorithms

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