The Certainty-Factor Model
David E. Heckerman · 2016
The certainty-factor (CF) model is a method for managing uncertainty in rule-based systems. Shortliffe and Buchanan (1975) developed the CF model in the mid-1970s for MYCIN, an expert system for the diagnosis and treatment of meningitis and infections of the blood. Since then, the CF model has become the standard approach to uncertainty management in rule-based systems. When the model was created, many artificial-intelligence (AI) researchers expressed concern about using Bayesian (or subjective) probability to represent uncertainty. Of these researchers, most were concerned about the practical limitations of using probability theory. In particular, information-science researchers were using the idiot-Bayes model to construct expert systems for medicine and other domains. This model included the assumptions that (1) faults or hypotheses were mutually exclusive and exhaustive, and (2) pieces of evidence were conditionally independent, given each fault or hypothesis. (See Bayesian Inference ∗This work was supported by the National Cancer Institute under Grant RO1CA51729-01A1.