Author(s)

Hany I. Fahmy, George Develekos, and Christos Douligeris

Abstract

Communication networks design is becoming increasingly complex, involving making networks more usable, affordable, and reliable. To help reduce this complexity, we have proposed, END, an Expert Network Designer for configuring, modeling, simulating, and evaluating large structured computer networks, employing artificial intelligence, knowledge representation and network simulation tools. In this paper, we present a neural network/knowledge acquisition machine-learning approach to improve END’s efficiency in solving the network design problem and to extend its scope to acquire new networking technologies, learn new network design techniques, and update the specifications of existing tech

Keywords