A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks

Sensors
Álvaro CarreraCarlos A Iglesias

Abstract

Traditionally, fault diagnosis in telecommunication network management is carried out by humans who use software support systems. The phenomenal growth in telecommunication networks has nonetheless triggered the interest in more autonomous approaches, capable of coping with emergent challenges such as the need to diagnose faults' root causes under uncertainty in geographically-distributed environments, with restrictions on data privacy. In this paper, we present a framework for distributed fault diagnosis under uncertainty based on an argumentative framework for multi-agent systems. In our approach, agents collaborate to reach conclusions by arguing in unpredictable scenarios. The observations collected from the network are used to infer possible fault root causes using Bayesian networks as causal models for the diagnosis process. Hypotheses about those fault root causes are discussed by agents in an argumentative dialogue to achieve a reliable conclusion. During that dialogue, agents handle the uncertainty of the diagnosis process, taking care of keeping data privacy among them. The proposed approach is compared against existing alternatives using benchmark multi-domain datasets. Moreover, we include data collected from a prev...Continue Reading

References

Jul 20, 2018·Psychological Review·Benjamin Eva, Stephan Hartmann

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Citations

Jun 3, 2021·Sensors·Néstor Alzate-MejíaJosé Roberto de Almeida-Amazonas

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Methods Mentioned

BETA
PISA

Software Mentioned

SMO
WEKA
NBTree
PISA
LADTree
J48
SMILE
BayesSearch
BARMAS
PART

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