Diagnosis of intermittent faults in Multi-Agent Systems: An SFL approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10477493" target="_blank" >RIV/00216208:11320/23:10477493 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=IJznzFVTrS" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=IJznzFVTrS</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.artint.2023.103994" target="_blank" >10.1016/j.artint.2023.103994</a>
Alternative languages
Result language
angličtina
Original language name
Diagnosis of intermittent faults in Multi-Agent Systems: An SFL approach
Original language description
Multi-Agent Systems (MAS) can be found in a wide variety of applications, including industrial systems, transportation, software systems and more. In such systems, agents may experience faults that affect the performance of the whole system. However, faulty agents might not consistently experience their fault, but rather in certain conditions. For example, a robot with a faulty rotating mechanism will appear healthy if it is tasked to only move in a straight line. Those faults are called Intermittent Faults. Such faults may cause the entire system to fail, but not always. Previous work proposed diagnosis algorithms for MAS, assuming faulty agents persistently behave abnormally. To the best of our knowledge, intermittent faults in MAS have not been concretely explored. In this paper we formally present a novel problem called Diagnosis of Intermittent Faults in Multi-Agent Systems (DIFMAS): a group of agents are observed across multiple runs. In each run, the success/failure of the agents and the system is observed, aiming to explain all the failed runs by diagnosing which agent(s) are faulty. The contributions of this paper are: (1) formalizing DIFMAS as a Model-Based Diagnosis problem, (2) solving it by presenting a Spectrum-Based Fault Localization (SFL) based method, called Multi-Run SFLbased Diagnosis Algorithm (MRSD). Experiments demonstrate that MRSD's outperforms competing SFL-based algorithms. Moreover, the algorithm's performance increases if planned interactions are considered.(c) 2023 Elsevier B.V. All rights reserved.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA23-05104S" target="_blank" >GA23-05104S: Multi-Robotic Path Planning and Execution</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Artificial Intelligence
ISSN
0004-3702
e-ISSN
1872-7921
Volume of the periodical
324
Issue of the periodical within the volume
November
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
22
Pages from-to
1-22
UT code for WoS article
001104155600001
EID of the result in the Scopus database
2-s2.0-85172255298