Computing with words

Computing with words is defined to be an extension of fuzzy logic, which permits self-referential generalization. It entails the randomization of declarative knowledge, which yields procedural knowledge. Such randomization can occur at two levels. The first is termed weak randomization, which is essentially a domain-general pattern matching operation. The second is termed strong randomization, which entails the application of one rule set to the semantics of another possibly including itself. Strong randomization rests on top of weak randomization. Strong randomization is essentially a heuristic process. It is fully scalable, since it can in theory map out its own needed heuristics for ever-more efficient search, including segmentation of the knowledge base. It is proven that strong learning must be knowledge-based, if effective. Computing with words does not preclude the use of predicate functions and procedural attachments. It may well be that the presented algorithm serves to unify programming and computing with words to yield an artificial intelligence