文档介绍：IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. NO. MARCH 1989 89 Knowledge Representation in Fuzzy Logic LOTFI A. ZADEH, FELLOW, IEEE (Invited Paper) Abstract-The conventional approaches to knowledge representa- tion, ., works, frames, predicate calculus, and Prolog, are based on bivalent logic. A serious ing of such approaches is their inability to come to grips with the issue of uncertainty and imprecision. As a consequence, the conventional approaches do not provide an adequate model for modes of reasoning which are approx- imate rather than exact. Most modes of human reasoning and all of common sense reasoning fall into this category. Fuzzy logic, which may he viewed as an extension of classical logical systems, provides an effective conceptual framework for dealing with the problem of knowledge representation in an environment of uncer- tainty and imprecision. Meaning representation in fuzzy logic is based on test-score semantics. In this semantics, a proposition is interpreted as a system of elastic constraints, and reasoning is viewed as elastic constraint propagation. Our paper presents a summary of the basic concepts and techniques underlying the application of fuzzy logic to knowledge representation and describes a number of examples relating to its use as putational system for dealing with uncertainty and imprecision in the context of knowledge, meaning, and inference. Zndex Terms-Approximate reasoning, fuzzy logic, knowledge rep- resentation. I. INTRODUCTION NOWLEDGE representation is one of the most basic K and actively researched areas of AI [4], [5], [30], [3 11, [36], [37], [39], [46], [47]. And yet, there are many important issues underlying knowledge representation which have not been adequately addressed. One such is- sue is th