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Attempts to replicate probabilistic reasoning in expert systems havetypically overlooked a critical ingredient of that process. Probabilisticanalysis typically requires extensive judgments regarding interdependenciesamong hypotheses and data, and regarding the appropriateness of variousalternative models. The application of such models is often an iterativeprocess, in which the plausibility of the results confirms or disconfirms thevalidity of assumptions made in building the model. In current expert systems,by contrast, probabilistic information is encapsulated within modular rules(involving, for example, "certainty factors"), and there is no mechanism forreviewing the overall form of the probability argument or the validity of thejudgments entering into it.

A Framework for Non-Monotonic Reasoning About Probabilistic Assumptions