The Representation of Fuzzy Knowledge

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Abstract
A new AI programming language (called FUZZY) is introduced which provides a number of facilities for efficiently representing and manipulating fuzzy knowledge. A fuzzy associative net is maintained by the system, and procedures with associated “procedure demons” may be defined for the control of fuzzy processes. Such standard AI language features as a pattern-directed data access and procedure invocation mechanism and a backtrack control structure are also available. This paper examines some general techniques for representing fuzzy knowledge in FUZZY, including the use of the associative net for the explicit representation of fuzzy sets and fuzzy relations, and the use of “deduce procedures” to implicitly define fuzzy sets, logical combinations of fuzzy sets, linguistic hedges, and fuzzy algorithms. The role of inference in a fuzzy environment is also discussed, and a technique for computing fuzzy inferences in FUZZY is examined. The programming language FUZZY is implemented in LISP, and is currently running on a UNIVAC 1110 computer.

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