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A Methodology for Uncertainty in Knowledge-Based Systems / by Kurt Weichselberger, Sigrid Pöhlmann.

By: Weichselberger, Kurt, 1929-Contributor(s): Pöhlmann, Sigrid, 1955-Material type: TextTextSeries: Serienbezeichnung | Lecture notes in computer science. Lecture notes in artificial intelligence ; ; 419.Publication details: Berlin, Heidelberg : Springer Berlin Heidelberg, 1990. Description: 1 online resource (VIII, 132 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9783540469643; 3540469648Subject(s): Computer science | Artificial intelligence | Distribution (Probability theory) | Statistics | Artificial intelligence | Computer science | Distribution (Probability theory) | StatisticsGenre/Form: Electronic books. Additional physical formats: Print version:: No titleDDC classification: 006.3 LOC classification: QA76.76.E95 | W44 1990TJ210.2-211.495Online resources: Click here to access online
Contents:
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.
Summary: 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.
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Includes bibliographical references.

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.

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.

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