This paper describes an application of an AI-based multiagent system to the management and diagnosis of TCP/IP-based intranet/intra-AS (autonomous system) computer networks. A copy of this system is attached to each network segment and is made responsible for that segment. It captures packets in the promiscuous mode and analyzes their data in real time. Based on this analysis, the data needed to manage the local network are obtained, any changes in the local network or network components are recognized, and problems are detected. When a problem is reported by a user or detected by the system, the problem is diagnosed cooperatively or autonomously depending on its type. The activities of the agents are coordinated based on the concepts of coordination levels and functional organizations. An example of cooperative diagnosis clarifies why this multiagent approach is essential for network management.
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