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Fuzzy rules in knowledge-based systems

Modelling gradedness, uncertainty and preference

  • Chapter
An Introduction to Fuzzy Logic Applications in Intelligent Systems

Part of the book series: The Springer International Series in Engineering and Computer Science ((SECS,volume 165))

Abstract

The paper starts with ideas of possibility qualification and certainty qualification for specifying the possible range of a variable whose value is ill-known. The notion of possibility which is used for that purpose is not the standard one in possibility theory, although the two notions of possibility can be related. Based on these considerations four distinct types of rules with different semantics involving gradedness and uncertainty are then introduced. The combination operations which appear for taking advantage of the available knowledge are all derived from the intended semantics of the rules. The processing of these four types of rules is studied in detail. Fuzzy rules modelling preference in decision processes are also discussed.

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Author information

Authors and Affiliations

  1. Institut de Recherche en Informatique de Toulouse, Université Paul Sabatier, 118 route de Narbonne, Toulouse Cedex, 31062, France

    Didier Dubois & Henri Prade

Authors
  1. Didier Dubois
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  2. Henri Prade
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Editor information

Editors and Affiliations

  1. Iona College, USA

    Ronald R. Yager

  2. University of California, Berkeley, USA

    Lotfi A. Zadeh

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© 1992 Springer Science+Business Media New York

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Dubois, D., Prade, H. (1992). Fuzzy rules in knowledge-based systems. In: Yager, R.R., Zadeh, L.A. (eds) An Introduction to Fuzzy Logic Applications in Intelligent Systems. The Springer International Series in Engineering and Computer Science, vol 165. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3640-6_3

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  • DOI: https://doi.org/10.1007/978-1-4615-3640-6_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-6619-5

  • Online ISBN: 978-1-4615-3640-6

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