A real time control strategy for Bayesian belief networks with application to ship classification problem solving

Abstract

Efficient ways to prioritize and gather evidence within belief networks are discussed. The authors also suggest ways in which one can structure a large problem (a ship classification problem in the present case) into a series of small ones. This both re-defines much of the control strategy into the system structure and also localizes run-time control issues into much smaller networks. The overall control strategy thus includes the combination of both of these methods. By combining them correctly one can reduce the amount of dynamic computation required during run-time, and thus improve the responsiveness of the system. When dealing with the ship classification problem, the techniques described appear to work well.<>