Agent-oriented techniques for network supervision

This paper presents the results of the Réseau futé (smart net) project, the goal of which is to use distributed AI and multi-agent techniques for network management and supervision. More precisely, these techniques have been applied to the partial automation of the dynamic processing (what is known about a network is always incomplete and can change at any time) of alarms and of various event notifications received by network management platforms. The system that we propose is able for example to automatically handle some alarms or to filter events of no-interest for a given operator. To achieve this goal, an assistant, or interface agent according to the model proposed by Patti Maes MK93], has been realized. The goal of the assistant is first to learn, by observation, the behavior of the network supervision operator and second to reproduce such a behavior when the conditions in which the behavior has been learned are detected again. The learned information are stored using chronicles Gha94]. A chronicle is a data-structure allowing programmers to represent sequences of events while taking temporal knowledge into account. Our assistant has been implemented and tested within Magenta which is a program, written in Smalltalk, that simulates (in a simplified way) a network management platform. This program respects roughly the gdmo and cmis standards.