Fuzzy set theory makes sense again when you realize it's not about classifying uncertainty or partial truth at all, but merely denoting partial set membership.

Fuzzy set theory makes sense again when you realize it's not about classifying uncertainty or partial truth at all, but merely denoting partial set membership. Goertzel's et aliorum PLN book and framework that I mentioned before use fuzzy values in exactly that way actually. There's a brief high-level overview on the differences between PLN and other approaches to uncertain inference early on in the book. In PLN, all of these four sentences are uniquely distinguished, whereas in a fuzzy framework it's not so clear what's going on beyond the first sentence: "Jim belongs to degree 0.6 to the fuzzy set of tall people.", "Jim shares 0.6 of the properties shared by people belonging to the set of tall people (where the different properties may be weighted).", "Jim has a 0.6 chance of being judged as belonging to the set of tall people, once more information about Jim is obtained (where this may be weighted as to the degree of membership that is expected to be estimated once the additional information is obtained).", and "Jim has an overall 0.6 amount of tallness, defined as a weighted average of extensional and intensional information."