A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks.

Area of Science:

• Computer Science
• Artificial Intelligence
• Network Engineering

Background:

• Traditional fault diagnosis in telecommunication networks relies on human experts and software support.
• Rapid network growth necessitates autonomous solutions for complex fault root cause analysis under uncertainty and data privacy constraints.

Purpose of the Study:

• To present a distributed fault diagnosis framework for telecommunication networks that addresses uncertainty and data privacy.
• To enable autonomous fault diagnosis through agent collaboration and argumentation.

Main Methods:

• A framework for distributed fault diagnosis utilizing an argumentative multi-agent system.
• Bayesian networks are employed as causal models for inferring fault root causes from network observations.
• Agents engage in argumentative dialogue to handle diagnostic uncertainty and maintain data privacy.

Main Results:

• The proposed framework demonstrates suitability for autonomous fault diagnosis in telecommunication networks.
• Performance validated against benchmark datasets and a real-world telecommunication network fault diagnosis system.
• Effective handling of uncertainty and data privacy during the distributed diagnosis process.

Conclusions:

• The developed framework offers a robust and autonomous solution for fault diagnosis in large-scale, distributed telecommunication environments.
• The integration of argumentative multi-agent systems and Bayesian networks provides a reliable approach to complex network fault analysis.
• The approach successfully balances diagnostic accuracy with critical data privacy requirements.