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Uncertainty Measures in Expert Systems
Author(s) -
Alvarez E.,
Castillo E.
Publication year - 1994
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.1994.tb00343.x
Subject(s) - axiom , extension (predicate logic) , meaning (existential) , cover (algebra) , subjective logic , computer science , risk analysis (engineering) , mathematics , artificial intelligence , epistemology , engineering , probabilistic logic , medicine , philosophy , geometry , mechanical engineering , programming language
In classic logic, premises, conclusions, and rules are treated deterministically, i.e., they are considered as either true or false. However, when dealing with reality, one finds that all these elements must be considered as uncertain. Thus classic logic must be extended to cover real situations. One possible extension is given by uncertainty measures together with aggregation formulas that combine the uncertainty of premises with that of the rules to obtain the uncertainty of conclusions. This paper describes different uncertainty measures, giving the physical meaning of the implied axioms and their limitations, illustrated by some examples. Finally, a classification of some of the well‐known uncertainty measures, such as belief and plausibility functions, probabilities, necessities, and possibilities, is given.