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Probabilistic and Non-Probabilistic Representation of Unctertainties in Expert Systems

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Mathematical Models for Decision Support

Part of the book series: NATO ASI Series ((NATO ASI F,volume 48))

Abstract

The definitions of “expert systems” vary considerably. Rather than specifying here which of the numerous definitions we accept, or introducing an additional definition, we shall just mention a number of features of expert systems, which might be accepted by most designers and users of expert systems and which seem to be of relevance with respect to fuzzy sets:

  1. a.

    The intended area of application is restricted as to its scope, ill-structured and uncertain. Therefore, no welldefined algorithms are available or are efficient enough to solve the problem.

  2. b.

    The system is — at least partly — based on expert knowledge which is either embodied in the system or which can be obtained from an expert and be analysed, stored and used by the system.

  3. c.

    The system has some inference capability suitable to use the knowledge — in whatever way it is stored — to draw conclusions.

  4. d.

    The interfaces to the user on one side and the expert on the other side should be such that the expert system can be used directly and that no “human interpreter” is needed between the system and the user or expert.

  5. e.

    Since heuristic elements are contained in an expert system, no optimality or correctness guarantee is provided. Therefore and in order to increase the acceptance by the user, it is considered to be at least desirable that the system contains a “justification” or “explaining” module.

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Authors and Affiliations

  1. RWTH Aachen, 5100, Aachen, The Federal of Republic

    H.-J. Zimmermann

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  1. H.-J. Zimmermann
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Editors and Affiliations

  1. Department of Mathematics and Statistics Brunel, The University of West London, Uxbridge, Middlesex, UB8 3PH, UK

    Gautam Mitra

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Zimmermann, HJ. (1988). Probabilistic and Non-Probabilistic Representation of Unctertainties in Expert Systems. In: Mitra, G., Greenberg, H.J., Lootsma, F.A., Rijkaert, M.J., Zimmermann, H.J. (eds) Mathematical Models for Decision Support. NATO ASI Series, vol 48. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83555-1_35

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  • DOI: https://doi.org/10.1007/978-3-642-83555-1_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-83557-5

  • Online ISBN: 978-3-642-83555-1

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