Fuzzy systems are mathematically based systems that enable computers to handle vague, imprecise, or ambiguous information. Edited by two of the top names in this field and written by a team of international experts, here is the most up-to-date and complete compilation of articles in fuzzy logic research. All chapters are original works prepared specifically for this volume, including articles on applications and tools. Fuzzy Logic for the Management of Uncertainty covers many important topics, including: Developments in mathematics that have paved the road for fuzzy logic; Deep, and of a broad perspective, exposition of virtually all approaches used in contemporary science for the representation and handling of imperfect (uncertain, imprecise, vague, ambiguous, etc.) information; Coverage of practically all relevant and promising directions and approaches in fuzzy logic research including LT--fuzzy logic, model theoretic approaches, intuitionistic fuzzy logic, nonmonotonic fuzzy logic, modifier fuzzy logic; VLSI fuzzy logic-based chips that have triggered the implementation of fuzzy logic in so many fields of science and technology; A broad coverage of fuzzy logic in approximate reasoning, including basic issues related to the role of fuzzy logic for approximate reasoning, analyses of various definitions of fuzzy implication that is a crucial element in fuzzy logic-based reasoning schemes, general issues related to reasoning and inference based on fuzzy logic, inconsistencies and explanations in imprecise reasoning and databases; Use of fuzzy logic for knowledge representation, including the use of fuzzy relations, fuzzy linguistic modifiers, etc.; Use of fuzzy logic for knowledge acquisition and elicitation, mainly for machine learning; Use of fuzzy logic for the modelling of neurons and neural networks; Use of fuzzy logic for the development of fuzzified higher level computer languages, notably for the fuzzification of LISP; Use of fuzzy logic for the management of uncertainty in implemented knowledge-based systems; Use of fuzzy logic for the validation of knowledge-based systems; Use of fuzzy logic in intelligent database management systems.

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