The problem of modeling uncertainty and inexact reasoning inrule-based expert systems is challenging on nonnative as well oncognitive grounds. First, the modular structure of the rule-basedarchitecture does not lend itself to standard Bayesianinference techniques. Second, there is no consensus...

Rule based expert systems deal with inexact reasoning through avariety of quasi-probabilistic methods, including the widely usedcertainty factors (CF) and subjective Bayesian (SB) models, versionsof which are implemented in many commercial expert systemshells. Previous research established that...

Rule-based expert systems must deal with uncertain data,subjective expert opinions, and inaccurate decision rules. Computer scientistsand psychologists have proposed and implemented a number of belief languages widely used in applied systems, and their normative validity is clearly an important...