FUNDAMENTALS OF UNCERTAINTY CALCULI WITH APPLICATIONS TO FUZZY INFERENCE by Michel Grabisch, Hung T. Nguyen, E. A. Walker ( 9780792331759 )

Item Details

Title:

FUNDAMENTALS OF UNCERTAINTY CALCULI WITH APPLICATIONS TO FUZZY INFERENCE

By:

Michel Grabisch, Hung T. Nguyen, E. A. Walker

Format:

Hardback

List price:	£179.99
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ISBN 10:

0792331753

ISBN 13:

9780792331759

Publisher:

SPRINGER

Pub. date:

30 November, 1994

Edition:

1995 ed.

Series:

Theory and Decision Library B 30

Pages:

350

Description:

This text provides a background for uncertainty calculi, with an emphasis on fuzziness. The approach to modelling knowledge expressed in natural language is based upon the mathematical theory of uncertainty related to the fuzzy measures and integrals and their applications.

Synopsis:

With the vision that machines can be rendered smarter, we have witnessed for more than a decade tremendous engineering efforts to implement intelligent sys- tems. These attempts involve emulating human reasoning, and researchers have tried to model such reasoning from various points of view. But we know precious little about human reasoning processes, learning mechanisms and the like, and in particular about reasoning with limited, imprecise knowledge. In a sense, intelligent systems are machines which use the most general form of human knowledge together with human reasoning capability to reach decisions. Thus the general problem of reasoning with knowledge is the core of design methodology. The attempt to use human knowledge in its most natural sense, that is, through linguistic descriptions, is novel and controversial. The novelty lies in the recognition of a new type of un- certainty, namely fuzziness in natural language, and the controversality lies in the mathematical modeling process. As R. Bellman [7] once said, decision making under uncertainty is one of the attributes of human intelligence. When uncertainty is understood as the impossi- bility to predict occurrences of events, the context is familiar to statisticians. As such, efforts to use probability theory as an essential tool for building intelligent systems have been pursued (Pearl [203], Neapolitan [182)). The methodology seems alright if the uncertain knowledge in a given problem can be modeled as probability measures.

Illustrations:

XII, 350 p.

Publication:

Netherlands

Imprint:

Springer

Returns:

Returnable

Some other items by this author:

A COURSE IN STOCHASTIC PROCESSES (HB)

A COURSE IN STOCHASTIC PROCESSES (PB)

A FIRST COURSE IN FUZZY AND NEURAL CONTROL