The Roles of Soft Computing and Fuzzy Logic in the Conception, Design and Deployment of Intelligent System
저자
Zadeh, Lotfi A. (Dept. of Eecs, univ., of California, Berkeley)
발행기관
학술지명
권호사항
발행연도
1996
작성언어
English
KDC
569.000
자료형태
학술저널
발행기관 URL
수록면
3-4(2쪽)
제공처
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 general than that of a cluster.
Modes of information granulation in which granules are crisp play important roles in a wide variety of methods, approaches and techniques. Among them are: interval analysis, quantization, rough set theory, 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 granules 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 attributes, e.g., in the case of the fuzzy granule hair, the fuzzy attributes are color, length, texture, etc. In turn, each of the fuzzy attributes 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 and 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 methodology 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.
In CW, the initial data set (IDS) is assumed to consist of a collection of propositions expressed in a natural language. The result of computation -- referred to as the terminal data set (TDS) -- is likewise a collection of propositions expressed in a natural language. To infer TDS from IDS the rules of inference in fuzzy logic are used for constraint propagation from premises to conclusions.
There are two main rationales for computing with words. First, computing with words is a necessity when the available information is not precise enough to justify the use of numbers. And second, computing with words is advantageous when there is a tolerance for imprecision, uncertainty and partial truth that can be exploited to achieve tractability, robustness, low solution cost and better rapport with reality. In coming years, computing with words is likely to evolve into an important methodology in its own right with wide-ranging applications on both basic and applied levels.
Inspired by the ways in which humans granulate human concepts, we can proceed to granulate conceptual structures in various fields of science. In a sense, this is what motivates computing with words. An intriguing possibility is to granulate the conceptual structure of mathematics. This would lead to what may be called granular mathematics. Eventually, granular mathematics may evolve into a distinct branch of mathematics having close links to the real world.
In the final analysis, fuzzy information granulation is central to human reasoning and concept formation. It is this aspect of fuzzy IG that underliesits essential role in the conception and design of intelligent systems. What is conclusive is that there are many, many tasks which humans can perform with ease and that no machine could perform without the use of fuzzy information granulation, This conclusion has a thought-provoking implication for AI. Without the methodology of fuzzy IG in its armamentarium, AI cannot achieve its goals.
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