The Roles Conception, of Soft Computing and Fuzzy Logic in the Design and Deployment of Intelligent System
L.A. Zadeh · 1996
The concepts of granulation and organization play fundamental roles in human cognition. In a general setting, granulation involves a decomposition of whole into parts. Conversely, organization involves an integration of parts into whole. In more specific terms, information granulation (IG) relates to partitioning a class of points (objects) into granules, with a granule being a clump of points drawn together by indistinguishability, similarity or functionality. The concept of a granule is more geneml than that of a cluster. Modes of information granulation in which granules are crisp play important roles in a wide variety of methods, approaches ancl techniques. Among them are:interval analysis, quantization, rough set theoy, diakoptics, divide and conquer, Dempster-Shafer theory, machine learning from examples, chunking, qualitative process theory, decision trees, semantic networks, analog-to-digital conversion, constraint programming, cluster analysis and many others. Important though it is, crisp information granulation (crisp IG) has a major blind spot. More specifically, it fails to reflect the fact that in much -- perhaps most -- of human reasoning and concept formation mules are fuzzy rather than crisp. For example, fuzzy granules of a human head are the nose, forehead, hair, cheeks, etc. Each of the fuzzy granules is associated with a set of fuzzy amiutes, e.g., in the case of the fuzzy granule hair, the fuzzy attributes are color, length, texture, etc. In turn, each of the fuzzy attriiutes is associated with a set of fuzzy values. Specifically, in the case of the fuzzy attribute length(hair), the fuzzy values are long, short, not very long, etc. The fuzziness of granules is characteristic of the ways in which human concepts are formed, organized ancl manipulated. In human cognition, fuzziness of granules is a direct consequence of fuzziness of the concepts of indistinguishability, similarity and functionality. Furthermore, it is entailed by the finite capacity of the human mind to store information and resolve detail. In this perspective, fuzzy information granulation (fuzzy IG) may be viewed as a form of lossy data compression. Fuzzy information granulation underlies the remarkable human ability to make rational decisions in an environment of imprecision, uncertainty and partial truth. And yet, despite its intrinsic importance, fuzzy information granulation has received scant attention except in the context of fuzzy logic, in which fuzzy IG underlies the basic concepts of linguistic variable, fuzzy if-then rule and fuzzy graph. In fact, the effectiveness and successes of fuzzy logic in dealing with real-world problems rest in large measure on the use of the machinery of fuzzy information granulation. This machinery is unique to fuzzy logic. Recently fuzzy information granulation has come to play a central role in the methodologv of computing with words. More specifically, in a natural language words play the role of labels of fuzzy granules. In computing with words, a proposition is viewed as an implicit fuzzy constraint on an implicit variable. The meaning of a proposition is the constraint which it represents.