Many classification problems must be performed in a timely or time constrained manner. For this reason, the generation of control schemes which are capable of responding in real-time are fundamental to many applications. For our problem, that of ship classification, tactical scenarios often dictate the response time required from a system. In this paper, we discuss efficient ways to prioritize and gather evidence within belief networks. We also suggest ways in which we can structure our large problem into a series of small ones. This both pre-defines much of our control strategy into the system structure and also localizes our 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 we can reduce the amount of dynamic computation required during run-time and thus improve the responsiveness of the system.
['Scott A. Musman', 'LiWu Chang', 'Lashon B. Booker']
APPLICATION OF A REAL-TIME CONTROL STRATEGY FOR BAYESIAN BELIEF NETWORKS TO SHIP CLASSIFICATION PROBLEM SOLVING