Approximate reasoning is a key motivation in fuzzy sets and possibility
theory. This volume provides a coherent view of this field, and its
impact on database research and information retrieval. First, the
semantic foundations of approximate reasoning are presented. Special
emphasis is given to the representation of fuzzy rules and specialized
types of approximate reasoning. Then syntactic aspects of approximate
reasoning are surveyed and the algebraic underpinnings of fuzzy
consequence relations are presented and explained. The second part of
the book is devoted to inductive and neuro-fuzzy methods for learning
fuzzy rules. It also contains new material on the application of
possibility theory to data fusion. The last part of the book surveys the
growing literature on fuzzy information systems. Each chapter contains
extensive bibliographical material.
Fuzzy Sets in Approximate Reasoning and Information Systems is a major
source of information for research scholars and graduate students in
computer science and artificial intelligence, interested in human
information processing.