Intelligent Agents for Probabilistic . . .

Abstract

As networks increase in size, heterogeneity and complexity, the need to maintain their availability and reliability grows in both importance and difficulty. In this paper, we propose a probabilistic distributed approach to automatic network fault diagnosis and correction in Wide Area Networks (WANs) using Bayesian networks. Each sub-domain of a WAN is assigned to a single agent that models its own partial knowledge as a Bayesian sub-net. When the agent is notied of an anomaly in one of its managed objects, it computes an initial optimal observation plan given reported abnormal observations. The plan species the order in which the objects are to be observed. Then, information gathering objects are sequentially evaluated using the myopic value of information, and the agent decides upon the object to observe based on its potential to reveal useful information about which component is faulty, and associated cost. To derive the globally optimal restoration plan, agents maintain an interfa..