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This paper examines the biases and performance of several uncertain inferencesystems: Mycin, a variant of Mycin. and a simplified version of probabilityusing conditional independence assumptions. We present axiomatic arguments forusing Minimum Cross Entropy inference as the best way to do uncertaininference. For Mycin and its variant we found special situations where itsperformance was very good, but also situations where performance was worse thanrandom guessing, or where data was interpreted as having the opposite of itstrue import We have found that all three of these systems usually gave accurateresults, and that the conditional independence assumptions gave the most robustresults. We illustrate how the Importance of biases may be quantitativelyassessed and ranked. Considerations of robustness might be a critical factor isselecting UlS's for a given application.

Experimentally Comparing Uncertain Inference Systems to Probability