Titlebook: Representing Uncertain Knowledge; An Artificial Intell Paul Krause,Dominic Clark Book 1993 Springer Science+Business Media Dordrecht 1993 F

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The Certainty Factor Model,bacteremia and meningitis (Shortliffe, 1976; Buchanan and Shortliffe, 1984). It is sometimes considered a type of modified Bayesian system. However, it differs from Bayesian systems in a number of respects, most notably in its use of a mathematical calculus within a goal-driven rule-based inference procedure.

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Epistemic Probability: the Dempster-Shafer theory of evidence,e that a numerical measure of epistemic . should be strictly governed by the frequentistic laws of . The Dempster-Shafer theory of evidence provides an alternative, more general, model for the assessment of numerical degrees of belief.

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The Nature of Uncertainty,, problem solving, decision making, classification and many others dealing with real world entities and data. Consequently, the management of uncertainty is central to the development of computer based systems that can successfully execute these tasks. This, in turn, depends upon the adoption of unc

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The Certainty Factor Model,based Expert Systems. It has been criticised on a number of grounds, as we shall see. However, it is an interesting example of a heuristic approach which attempts to weaken some of the axioms of probabilistic approaches and be computationally efficient. This approach was first employed in Mycin. Myc

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Reasoning with Imprecise and Vague Data,the use of a set theoretic component in the model. We will revisit the Dempster-Shafer theory in order to discuss its handling of imprecision, and introduce fuzzy sets and possibility theory in connection with vagueness.

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Non-monotonic Logic,n necessarily follows from a set of premises. It was perhaps in keeping with the Greek obsession with absolutes that there was to be no uncertainty or doubt associated with these arguments. The arguments were used to establish facts; they were deductive proofs. Indeed Aristotle held that deductive p

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