## Evidential Confirmation As Transformed Probability (1986)

#### by Benjamin N. Grosof

**Abstract**:
A considerable body of work in AI has been concerned with aggregating
measures of confirmatory and disconfirmatory evidence for a common set of
propositions. Claiming classical probability to be inadequate or
inappropriate, several researchers have gone so far as to invent new formalisms
and methods. We show how to represent two major such alternative approaches
to *evidential confirmation* not only in terms of
transformed (Bayesian) probability, but also in terms of each other.
This unifies two of the leading approaches to confirmation theory, by showing
that a revised MYCIN Certainty Factor method is equivalent to a special
case of Dempster-Shafer theory. It yields a well-understood axiomatic basis,
i.e., conditional independence, to interpret previous work on quantitative
confirmation theory. It substantially resolves the "take-them-or-leave-them"
problem of priors: MYCIN had to leave them out, while PROSPECTOR had to
have them in. It recasts some of confirmation theory's advantages in terms of
the psychological accessibility of probabilistic information in different
(transformed) formats. Finally, it helps to unify the representation of
uncertain reasoning.

Last update: **1-8-98**

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