A Probabilistic Causal Model for Diagnostic Problem Solving Part II: Diagnostic Strategy

An important issue in diagnostic problem solving is how to generate and rank plausible hypotheses for a given set of manifestations. Since the space of possible hypotheses can be astronomically large if multiple disorders can be present simultaneously, some means is required to focus an expert system´s attention on those hypotheses most likely to be valid. A domain-independent algorithm is presented that uses symbolic causal knowledge and numeric probabilistic knowledge to generate and evaluate plausible hypotheses during diagnostic problem solving. Given a set of manifestations known to be present, the algorithm uses a merit function for partially completed competing hypotheses to guide itself to the provably most probable hypothesis or hypotheses.