An information theoretic technique to design belief network based expert systems

Abstract

This paper addresses the problem of constructing belief network based expert systems. We discuss a design tool that assists in the development of such expert systems by comparing alternative representations. The design tool uses information theoretic measures to compare alternative structures. Three important capabilities of the design tool are discussed: (i) evaluating alternative structures based on sample data; (ii) finding optimal networks with specified connectivity conditions; and (iii) eliminating weak dependencies from derived network structures. We have examined the performance of the design tool on many sets of simulated data, and show that the design tool can accurately recover the important dependencies across variables in a problem domain. We illustrate how this program can be used to design a belief network for evaluating the financial distress situation for banks.