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Knowledge Representation in Fuzzy Logic
March 1989 (vol. 1 no. 1)
pp. 89-100

The author presents a summary of the basic concepts and techniques underlying the application of fuzzy logic to knowledge representation. He then describes a number of examples relating to its use as a computational system for dealing with uncertainty and imprecision in the context of knowledge, meaning, and inference. It is noted that one of the basic aims of fuzzy logic is to provide a computational framework for knowledge representation and inference in an environment of uncertainty and imprecision. In such environments, fuzzy logic is effective when the solutions need not be precise and/or it is acceptable for a conclusion to have a dispositional rather than categorical validity. The importance of fuzzy logic derives from the fact that there are many real-world applications which fit these conditions, especially in the realm of knowledge-based systems for decision-making and control.

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Index Terms:
fuzzy logic; knowledge representation; computational system; uncertainty; imprecision; meaning; inference; real-world applications; knowledge-based systems; decision-making; control; fuzzy logic; knowledge representation
Citation:
L.A. Zadeh, "Knowledge Representation in Fuzzy Logic," IEEE Transactions on Knowledge and Data Engineering, vol. 1, no. 1, pp. 89-100, March 1989, doi:10.1109/69.43406
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