Toward an empirical approach to evaluating the knowledge base of an expert system

A general approach is described to the test and evaluation of an expert system knowledge base that involves the use of a representative set of randomly selected test problems and the application of well-defined statistical measures of accuracy and bias for each node in an inference network. Using the analogy that each node can be treated as a signal detector, some elements of a mathematics consistent with the approach are developed. >