Constructing Diagnostic Explanations Using Schema-Structured Bayesian Networks

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The goal of this research is to develop a representation and algorithms that characterize diagnostic reasoning. Human experts in a problem domain frequently interpret data in that domain in the context of a particular causal hypothesis. This type reasoning contrasts with deductive inference where from a set of general rules and facts further information is deduced by sound inference rules. Diagnostic reasoning, often called abductive inference, however, moves from a set of facts to the "best explanation" for the existence of these facts. This process often requires the expert to make a hypothetical conjecture that would explain the facts and then search for specific new information that can confirm that conjecture. Thus the human expert searches through a space of possible explanations for the observed information. This research uses Bayesian Belief Networks to build causal models of a domain. This approach represents in a precise way the interrelationship of causal patterns and their use in moving towards an explanation. The research supports handling of conflicting and ambiguous evidence, as well as a clear method for rating plausible inferences and the possibility of learning relative strengths of conditional probabilities from available statistical data. Although the research domain is built on data from investigation of failures of discrete component semiconductors as well as the analysis of failure mechanisms in complex real time control, diagnostic reasoning is a general research area. Results could be important in modeling medical decision-making, integrated circuit fault analysis, as well as used in real time process monitoring and control. http://www.cs.unm.edu/CS_Dept/faculty/homepage/luge r/