In the evolution of scientific theories, concern with uncertainty is almost invariably a concomitant of maturation. This is certainly true of the evolution· of physics, economics, operations research, communication sciences, and a host of other fields. And it is true of what has been happening more recently in the area of artificial intelligence, most notably in the development of theories relating to the management of uncertainty in knowledge-based systems. In science, it is traditional to deal with uncertainty through the use of probability theory. In recent years, however, it has become increasingly clear that there are some important facets of uncertainty which do not lend themselves to analysis by classical probability-based methods. One such facet is that of lexical elasticity, which relates to the fuzziness of words in natural languages. As a case in point, even a simple relation X, Y, and Z, expressed as if X is small and Y is very large then between Z is not very small, does not lend itself to a simple interpretation within the framework of probability theory by reason of the lexical elasticity of the predicates small and large.

Possibility Theory: An Approach to Computerized Processing of Uncertainty

Science & Math

Dubois, Didier

Mathematics

Springer

Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference

Wellman, Michael P.

D'Ambrosio, Bruce

Smets, Phillipe

Computers & Technology

Morgan Kaufmann Pub

Principles Of Knowledge Representation And Reasoning

Williams, Mary-Anne

International Conference On Principles Of Knowledge Representation And Reasoning (9th : 2004 : Whistler, B.c.)

Welty, Chris

AAAI Press

This short book provides a unified view of the history and theory of random sets and fuzzy random variables, with special emphasis on its use for representing higher-order non-statistical uncertainty about statistical experiments. The authors lay bare the existence of two streams of works using the same mathematical ground, but differing form their use of sets, according to whether they represent objects of interest naturally taking the form of sets, or imprecise knowledge about such objects. Random (fuzzy) sets can be used in many fields ranging from mathematical morphology, economics, artificial intelligence, information processing and statistics per se, especially in areas where the outcomes of random experiments cannot be observed with full precision. This book also emphasizes the link between random sets and fuzzy sets with some techniques related to the theory of imprecise probabilities.This small book is intended for graduate and doctoral students in mathematics or engineering, but also provides an introduction for other researchers interested in this area. It is written from a theoretical perspective. However, rather than offering a comprehensive formal view of random (fuzzy) sets in this context, it aims to provide a discussion of the meaning of the proposed formal constructions based on many concrete examples and exercises. This book should enable the reader to understand the usefulness of representing and reasoning with incomplete information in statistical tasks.  Each chapter ends with a list of exercises.

Random Sets and Random Fuzzy Sets as Ill-Perceived Random Variables: An Introduction for Ph.D. Students and Practitioners (SpringerBriefs in Computational Intelligence)

Couso, Inés

Sánchez, Luciano

Computer Science

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