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AAAI 2023

Distributed Spectrum-Based Fault Localization

Conference Paper AAAI Technical Track on Knowledge Representation and Reasoning Artificial Intelligence

Abstract

Spectrum-Based Fault Localization (SFL) is a popular approach for diagnosing faulty systems. SFL algorithms are inherently centralized, where observations are collected and analyzed by a single diagnoser. Applying SFL to diagnose distributed systems is challenging, especially when communication is costly and there are privacy concerns. We propose two SFL-based algorithms that are designed for distributed systems: one for diagnosing a single faulty component and one for diagnosing multiple faults. We analyze these algorithms theoretically and empirically. Our analysis shows that the distributed SFL algorithms we developed output identical diagnoses to centralized SFL while preserving privacy.

Authors

Keywords

  • KRR: Diagnosis and Abductive Reasoning
  • MAS: Coordination and Collaboration
  • MAS: Distributed Problem Solving

Context

Venue
AAAI Conference on Artificial Intelligence
Archive span
1980-2026
Indexed papers
28718
Paper id
355379874008654536