Coherent qualitative probability as an effective tool to represent uncertainty in the field of Artificial Intelligence is proposed. A suitable model to deal with vague and varying information is studied and some computable conditions are presented. An expert may introduce qualitative evaluations on a family of events containing only those strictly related to the problem. At any time the qualitative structure can be modified by referring to further events or relations or by better specifying the previously given ones. Coherence can be checked and (numerical) probabilistic statements, compatible with the qualitative judgements, can possibly be given.
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|Titolo:||Coherent qualitative probability and uncertainty in Artificial Intelligence|
|Data di pubblicazione:||1990|
|Appartiene alla tipologia:||04b Atto di convegno in volume|