Uncertainty in artificial intelligence: Is probability epistemologically and heuristically accurate?

Historically, probability has been by far the most widely used formalism for representing uncertainty. However, the majority of AI researchers have not, hitherto, found standard probabilistic techniques very appealing for use in rule-based, expert systems. Among the many alternative numerical schemes for quantifying uncertainty that have been developed are the Certainty Factors used in Mycin (Shortliffe & Buchanan, 1975) and its descendants, Fuzzy Set Theory (Zadeh, 1984), the quasi-probabilistic scheme of Prospector (Duda et al., 1976), and the Belief functions of Dempster-Shafer theory (Shafer, 1976). There have also been attempts to develop non-numerical schemes, including Paul Cohen’s theory of endorsements (Cohen, 1985), Doyle’s theory of reasoned assumptions (Doyle, 1983), and various linguistic representations of uncertainty (Fox, 1986). We shall refer to both probabilistic and alternative methods, generically, as uncertain inference schemes, or UISs.

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