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Minimalist Diagnosis of Discrete-Event Systems

Authors Gianfranco Lamperti , Marina Zanella

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Gianfranco Lamperti
  • Department of Information Engineering, University of Brescia, Italy
Marina Zanella
  • Department of Information Engineering, University of Brescia, Italy

Funding

We acknowledge the support of the PNRR project FAIR - Future AI Research (PE00000013), Spoke 9 - Green-aware AI, under the NRRP MUR program funded by the NextGenerationEU, specifically by the project Argumentation for Informed Decisions with Applications to Energy Consumption in Computing – AIDECC (CUP D53C24000530001).

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Gianfranco Lamperti and Marina Zanella. Minimalist Diagnosis of Discrete-Event Systems. In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 12:1-12:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/OASIcs.DX.2024.12

Abstract

Model-based diagnosis of discrete-event systems (DESs) is afflicted by two major difficulties, the former being the huge size of the search space, which has a heavy impact on the processing time, the latter being a possibly large number of diagnoses explaining the perceived sequence of observations, which may cause a cognitive overload in human diagnosticians or even delays in post-processing. These difficulties add up and they are exacerbated in critical scenarios where an action must be taken in real-time. To make DES diagnosis viable in these contexts, a Minimalist Diagnosis Engine is presented, which is based on a parsimony principle: instead of computing the set of all diagnoses inherent to the given sequence of observations, only minimal diagnoses are elicited as candidates. Since in this paper, as in most contributions on model-based diagnosis of DESs in the literature, a diagnosis is defined as a set of faults, minimal diagnoses are subset minimal. The proposal is justified since minimal diagnoses are suitable for DESs, and since the new diagnosis engine is able to prune the search space, thus reducing the computation effort with respect to a sound and complete method. Moreover, in order to further decrease the execution time, whenever the method is dealing with a new observation, it performs online a (partial) knowledge-compilation so as the portions of the DES space that have already been processed and transformed into chunks of compiled knowledge can speed up the next abductive reasoning steps, relevant to the upcoming observations.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Causal reasoning and diagnostics
Keywords
  • model-based reasoning
  • diagnosis during monitoring
  • discrete-event systems
  • active systems
  • subset-minimal diagnosis
  • dynamical knowledge-compilation
  • minimalism
  • laziness

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