A knowledge-based strategy for the automated support to network management tasks

Sameera Abar, Tetsuo Kinoshita

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This paper presents a domain-ontology driven multi-agent based scheme for representing the knowledge of the communication network management system. In the proposed knowledge-intensive framework, the static domain-related concepts are articulated as the domain knowledge ontology. The experiential knowledge for managing the network is represented as the fault-case reasoning models, and it is explicitly encoded as the core knowledge of multi-agent middleware layer as heuristic production-type rules. These task-oriented management expertise manipulates the domain content and structure during the diagnostic sessions. The agents' rules along with the embedded generic java-based problem-solving algorithms and run-time log information, perform the automated management tasks. For the proof of concept, an experimental network system has been implemented in our laboratory, and the deployment of some test-bed scenarios is performed. Experimental results confirm a marked reduction in the management-overhead of the network administrator, as compared to the manual network management techniques, in terms of the time-taken and effort-done during a particular fault-diagnosis session. Validation of the reusability/modifiability aspects of our system, illustrates the flexible manipulation of the knowledge fragments within diverse application contexts. The proposed approach can be regarded as one of the pioneered steps towards representing the network knowledge via reusable domain ontology and intelligent agents for the automated network management support systems.

Original languageEnglish
Pages (from-to)774-788
Number of pages15
JournalIEICE Transactions on Information and Systems
Issue number4
Publication statusPublished - 2010


  • Distributed problem-solving
  • Domain ontology
  • Knowledge engineering
  • Multi-agent system
  • Network management

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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