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L.A. Zadeh, "Knowledge Representation in Fuzzy Logic," IEEE Transactions on Knowledge and Data Engineering, vol. 1, no. 1, pp. 89100, March, 1989.  
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@article{ 10.1109/69.43406, author = {L.A. Zadeh}, title = {Knowledge Representation in Fuzzy Logic}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {1}, number = {1}, issn = {10414347}, year = {1989}, pages = {89100}, doi = {http://doi.ieeecomputersociety.org/10.1109/69.43406}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  Knowledge Representation in Fuzzy Logic IS  1 SN  10414347 SP89 EP100 EPD  89100 A1  L.A. Zadeh, PY  1989 KW  fuzzy logic; knowledge representation; computational system; uncertainty; imprecision; meaning; inference; realworld applications; knowledgebased systems; decisionmaking; control; fuzzy logic; knowledge representation VL  1 JA  IEEE Transactions on Knowledge and Data Engineering ER   
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 realworld applications which fit these conditions, especially in the realm of knowledgebased systems for decisionmaking and control.
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