PLAUSIBLE REASONING IN CLASSIFICATION PROBLEM SOLVING

A prototype system for classifying complex ship images has convincingly demonstrated that Bayesian reasoning is a valuable tool for making plausible inferences about classificatory hypotheses given impoverished feature data¹. It remains to be shown that such methods are also useful in handling the large scale, resource-constrained classification problems that are of interest to the Navy. Classifying objects using sensor data inan operational environment is a demanding task. Regardless of the kind of sensor information available – visual,infrared,radar,or sonar – this is a task in which complex inferences must be made reliably under stringent computational constraints, and based on incomplete and uncertain evidence. This paper describes research efforts focused ondevising a robust and accurate classification problem solver that meets this challenge.