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OBJECTIVE PROBABILITIES IN EXPERT SYSTEMS
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DOI：10.1016/0004-3702(93)90067-L.png)
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In this paper we present a general methodology for handling uncertain knowledge in expert systems, which is based upon objective probability theory. The use of objective probabilities helps to overcome some of the difficulties in the subjective Bayesian approach. The basic idea is to refine a qualitative assessment of uncertainty made by a domain expert into a quantitative objective probability by measuring frequencies in data sets. Knowledge is represented as a probabilistic network where the structure is elucidated from the experts, and the probability distributions are estimated from a set of representative samples from the domain. We test the hypothesis of independence between variables using linear regression analysis techniques. Having identified dependencies we modify the structure of the network to account for them. We have tested our methodology by implementing an expert system for providing diagnostic advice during colon endoscopy. Our results show strong empirical evidence supporting our approach.
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