Connectionist Learning for Intelligent Configuration Systems: An Application to Telecommunication Service Provisioning

In this paper we describe a connectionist approach embedded in an i intelligent system to resolve the complex configuration problems. The use of neural networks to improve the configuration process provides several potential advantages over conventional approaches, including the ability to lear by example from the existing cases, to generate reasonably good approximate results from incomplete information, and to process large amounts of data efficiently and rapidly.