@article{a-gsi-article-20191,
author = "Carrera Barroso, {\'A}lvaro and Eduardo Alonso and Iglesias, Carlos A.",
abstract = "raditionally, 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 previous fault diagnosis system running in a telecommunication network for one and a half
years. Results show that the proposed approach is suitable for the motivational scenario.",
comments = "JCR  2019 Q1 3.275, SJR 2019 Q2 0.653, Scopus 2019 Q1 5.0  ",
issn = "1424-8220",
journal = "Sensors",
keywords = "bayesian networks;argumentation;telecommunication networks",
month = "August",
number = "15",
pages = "3408-3430",
title = "{A} {B}ayesian {A}rgumentation {F}ramework for {D}istributed {F}ault {D}iagnosis in {T}elecommunication {N}etworks",
url = "https://www.mdpi.com/1424-8220/19/15/3408/htm",
volume = "19",
year = "2019",
}