In this book the consequent use of probability theory is proposed for handling uncertainty in expert systems. It is shown that methods violating this suggestion may have dangerous consequences (e.g., the Dempster-Shafer rule and the method used in MYCIN). The necessity of some requirements for a correct combining of uncertain information in expert systems is demonstrated and suitable rules are provided. The possibility is taken into account that interval estimates are given instead of exact information about probabilities. For combining information containing interval estimates rules are provided which are useful in many cases.

Bibliographic Information

Book Title: A Methodology for Uncertainty in Knowledge-Based Systems
Authors: Kurt Weichselberger, Sigrid Pöhlmann
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/BFb0037513
Publisher: Springer Berlin, Heidelberg
eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 1990
Softcover ISBN: 978-3-540-52336-9Published: 07 March 1990
eBook ISBN: 978-3-540-46964-3Published: 22 January 2006
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: X, 310
Topics: Artificial Intelligence, Probability Theory and Stochastic Processes, Statistics, general

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Table of contents (7 chapters)

Front Matter

The aims of this study

Interval estimation of probabilities

Related theories

The simplest case of a diagnostic system

Generalizations

Interval estimation of probabilities in diagnostic systems

A demonstration of the use of interval estimation

Back Matter

Keywords

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