A methodology for computing with words

An alternative interpretation of linguistic variable is introduced together with the notion of a linguistic description of a value or set of values. T he latter is taken to be a fuzzy set on words where the membership values q uantify the suitability of a particular word as a label for the value or va lues being considered. This concept is then applied to reasoning with lingu istic quantifiers these being defined as linguistic descriptions of probabi lity values. From this viewpoint linguistic quantifiers are constraints on probability values and hence using the voting model and Bayesian methods in fer second order densities. In this respect such quantifiers can be view as an alternative form of imprecise probability. These ideas are then used in our proposed methodology for converting probabilistic inference rules into linguistic inference rules and a computationally cheap approximation algor ithm for such rules is then introduced. The approach is illustrated in a nu mber of worked examples using various types of rules including linguistic s yllogisms and a linguistic version of Jeffrey's rule. Finally a number of m ethods for information fusion with linguistically quantified statements are discussed. (C) 2001 Elsevier Science Inc. All rights reserved.