Efficient knowledge-based decisions in a time-stressed environment

The author focuses on establishing a methodology to find the answer to a data-driven knowledge-based system problem as quickly as possible on the basis of incomplete information. The primary objective is to gather as few initial condition values and resolve as few goal states as can be managed consistent with reaching a satisfactory answer. The concern is with opportunistically moving from a list of possibly heterogeneous states to one out of a collection of possibly heterogeneous goal states. Algorithms have been developed to make decisions based on incomplete information that progressively identify and resolve rules with the greatest potential final belief values. Uncertainty is managed so unnecessary oscillation between condition states does not occur