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The dynamic nature of Asynchronous Transfer Mode (ATM) networks management operations requires intelligent systems that can proactively assess large amounts of information and make real-time decisions. To perform at such a level, intelligent systems need to be autonomous and adaptive. This paper presents the results of our research, which employs collaborative real-time learning agents to poll critical ATM network element statistics while minimizing the impact of network monitoring activities.

Proteus*—adaptive polling system for proactive management of ATM networks using collaborative intelligent agents