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Greetings from Prof. Zadeh Soft Computing: (1997) 1 : (c) Springer-Verlag 1997 Editorial What is soft computing? Since the publication of my first paper on soft data analysis in 1981, the concept of soft computing has undergone many changes. In its latest incarnation, soft computing may be defined as follows. Soft Computing (SC) is an association of computing methodologies centering on fuzzy logic (FL), neurocomputing (NC), genetic computing (GC), and probabilistic computing (PC). The methodologies comprising soft computing are for the most part complementary and synergistic rather than competitive. The guiding principle of soft computing is: exploit the tolerance for impression, uncertainty, partial truth, and approximation to achieve tractability, robustness, low solution cost and better rapport with reality. One of the principle aims of soft computing is to provide a foundation for the conception, design, and application of intelligent systems employing its member methodologies symbolically rather than in isolation. Within soft computing, the main concerns of fuzzy logic, neurocomputing, genetic computing and probabilistic computing center on: FL: approximate reasoning, information granulation, computing with words, NC: learning, adaptation, classification, system modelling and identification, GC: synthesis, tuning and optimization through systematized random search and evolution, PC: management of uncertainty, belief networks, prediction, chaotic systems. As an association of computing methodologies, soft computing is certain to grow in visibility and importance in the years ahead. What is the rationale behind this expectation? In my view, a key reason is related to the growing realization that the conceptual structure of conventional, hard computing is much too precise in relation to the pervasive imprecision of the real world. In this context, there are two distinct issues that have to be considered. First, there are many real world problems which do not lend themselves to solution by the techniques of hard computing because the need information is not available and/or the systems under consideration are not sufficiently well defined. Such problems are the norm in economic planning, living systems, large-scale societal systems and human decision-making. Another source of such problems is AI, especially in the realms of commonsense reasoning, computer vision and natural language understanding. Indeed, it may be argued that it is the commitment of mainstream AI to hard computing and its coolness toward soft computing that impeded AI's ability to achieve the ambitious goals that were set at its inception. The other and perhaps more important reason is that employment of soft computing methodologies serves to exploit the tolerance for imprecision, uncertainty, partial truth and approximation. In so doing, soft computing mimics the remarkable human ability to make rational decisions in an environment of uncertainty and imprecision. A case in point is the problem of parking a car. The tolerance for imprecision in this problem makes it possible for humans to park a car without any measurement and any knowledge of system dynamics. Without exploiting the tolarance for imprecision, the parking problem becomes intractable for humans and very hard for machines. Exploitation of the tolerance for imprecision, uncertainty, partial truth and approximation plays a pivotal role in data compression, information retrieval and communication. In this realm, fuzzy logic plays a particularly important role by providing a methodology for dealing with information granulation and computing with words in ways that mimic human reasoning and concept formation. In essence, the role model for fuzzy logic is the human mind. An aspect of soft computing that is of central importance is the symbolic relationship between its constituent methodologies. What this implies is that in the solution of many problems-especially in the conception and design of intelligent systems-it is advantageous to employ a combination of two or more of the constituent methodologies of soft computing, leading to what is referred to as HYBRID INTELLIGENT SYSTEMS. Currently, the most visible systems of this type are neuro-fuzzy systems. However, we are also beginning to see a growing number of fuzzy-genetic, neuro-genetic and neuro-fuzzy-genetic systems. Such systems are likely to become ubiquitous in the not distant future. What is certain is that-in the years ahead-the advent of hybrid intelligent systems will have a profound impact on the ways in which intelligent systems are conceived, designed, employed and interacted with. Viewed in this perspective, the publication of Soft Computing is an important event in the crystallization of soft computing as a prominent component of modern science and information technology. The publication of Soft Computing realizes the vision of the editors, the authors and the publisher-a vision which could not become a reality without the invaluable initiative and support of the SGS-THOMSON Corporation. Lotfi A. Zadeh Editor-in-chief

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