Deep versus compiled knowledge approaches to diagnostic problem-solving

Most of the current generation expert systems use knowledge which does not represent a deep understanding of the domain, but is instead a collection o f "pattern --> action" rules, which correspond to the problem-solving heuri stics of the expert in the domain. There has thus been some debate in the f ield about the need for and role of "deep" knowledge in the design of exper t systems. It is often argued that this underlying deep knowledge will enab le an expert system to solve hard problems. In this paper we consider diagn ostic expert systems and argue that given a body of underlying knowledge th at is relevant to diagnostic reasoning in a medical domain, it is possible to create a diagnostic problem-solving structure which has all the aspects of the underlying knowledge needed for diagnostic reasoning "compiled" into it. It is argued this compiled structure can solve all the diagnostic prob lems in its scope efficiently, without any need to access the underlying st ructures. We illustrate such. a diagnostic structure by reference to our me dical system MDX. We also analyze the use of these knowledge structures in providing explanations of diagnostic reasoning.