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PUBLICATIONS

An Adaptable AI Assistant for Network Management

Published

Author(s)

Amar Abane, Abdella Battou, Mheni Merzouki

Abstract

This paper presents a network management AI assistant built with Large Language Models. It adapts at runtime to the network state and specific platform, leveraging techniques like prompt engineering, document retrieval, and Knowledge Graph integration. The AI assistant aims to simplify management tasks and is easily reproducible with available source code.
Proceedings Title
NOMS 2024-2024 IEEE Network Operations and Management Symposium
Conference Dates
May 6-10, 2024
Conference Location
Seoul, KR
Conference Title
IEEE/IFIP Network Operations and Management Symposium
Pub Type
Conferences

Download Paper

https://doi.org/10.1109/NOMS59830.2024.10574957
Local Download

Keywords

LLMs, text embeddings, RAG, network management, knowledge graph, Neo4j, graph database
Networking, Information technology and Artificial intelligence

Citation

Abane, A. , Battou, A. and Merzouki, M. (2024), An Adaptable AI Assistant for Network Management, NOMS 2024-2024 IEEE Network Operations and Management Symposium, Seoul, KR, [online], https://doi.org/10.1109/NOMS59830.2024.10574957, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957878 (Accessed October 9, 2025)

Issues

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Created July 3, 2024
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