  This paper addresses fundamental issues on the nature of the concepts and
structures of fuzzy logic, focusing, in particular, on the conceptual and
functional differences that exist between probabilistic and possibilistic
approaches. A semantic model provides the basic framework to define
possibilistic structures and concepts by means of a function that quantifies
proximity, closeness, or resemblance between pairs of possible worlds. The
resulting model is a natural extension, based on multiple conceivability
relations, of the modal logic concepts of necessity and possibility. By
contrast, chance-oriented probabilistic concepts and structures rely on
measures of set extension that quantify the proportion of possible worlds where
a proposition is true. Resemblance between possible worlds is quantified by a
generalized similarity relation: a function that assigns a number between O and
1 to every pair of possible worlds. Using this similarity relation, which is a
form of numerical complement of a classic metric or distance, it is possible to
define and interpret the major constructs and methods of fuzzy logic:
conditional and unconditioned possibility and necessity distributions and the
generalized modus ponens of Zadeh.
