Model-Based Diagnosis of Multi-Agent Systems: A Survey.

Meir Kalech, Avraham Natan

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

Abstract

As systems involving multiple agents are increasingly deployed, there is a growing need to diagnose failures in such systems. Model-Based Diagnosis (MBD) is a well known AI technique to diagnose faults in systems. In this approach, a model of the diagnosed system is given, and the real system
is observed. A failure is announced when the real system’s output contradicts the model’s expected output. The model then is used to deduce the defective components that explain the unexpected observation. MBD has been increasingly being deployed in distributed and multi-agent systems. In this
survey, we summarize twenty years of research in the field of model-based diagnosis algorithms for MAS diagnosis. We depict three attributes that should be considered when examining MAS diagnosis: (1) The objective of the diagnosis. Either diagnosing faults in the MAS plans or diagnosing coordination faults. (2) Centralized vs. distributed. The diagnosis method could be applied either by a centralized agent or by the agents in a distributed manner. (3) Temporal vs. nontemporal. Temporal diagnosis is used to diagnose the MAS’s
temporal behaviors, whereas non-temporal diagnosis is used to diagnose the conduct based on a single observation. We survey diverse studies in MBD of MAS based on these attributes, and provide novel research challenges in this field
for the AI community
Original languageEnglish
Title of host publicationAAAI
Pages12334-12341
Number of pages8
StatePublished - 2022

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