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Walley, Peter

Measures of uncertainty in expert systems. (English) Zbl 1506.68157

Artif. Intell. 83, No. 1, 1-58 (1996).
Summary: This paper compares four measures that have been advocated as models for uncertainty in expert systems. The measures are additive probabilities (used in the Bayesian theory), coherent lower (or upper) previsions, belief functions (used in the Dempster-Shafer theory) and possibility measures (fuzzy logic). Special emphasis is given to the theory of coherent lower previsions, in which upper and lower probabilities, expectations and conditional probabilities are constructed from initial assessments through a technique of natural extension. Mathematically, all the measures can be regarded as types of coherent lower or upper previsions, and this perspective gives some insight into the properties of belief functions and possibility measures. The measures are evaluated according to six criteria: clarity of interpretation; ability to model partial information and imprecise assessments, especially judgements expressed in natural language; rules for combining and updating uncertainty, and their justification; consistency of models and inferences; feasibility of assessment; and feasibility of computations. Each of the four measures seems to be useful in special kinds of problems, but only lower and upper previsions appear to be sufficiently general to model the most common types of uncertainty.

Cited in 65 Documents

MSC:

68T35 Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence
68T37 Reasoning under uncertainty in the context of artificial intelligence

Keywords:

inference; decision; prevision; Bayesian theory; Dempster-Shafer theory; belief functions; possibility theory; lower probability; upper probability; imprecise probabilities; conditional probability; independence

Software:

PULCinella
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Full Text: DOI

References:

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