Models for reasoning with multitype uncertainty in expert systems

  • J. C. A. van der Lubbe
  • E. Backer
  • W. Krijgsman
7. Hybrid Approaches To Uncertainty
Part of the Lecture Notes in Computer Science book series (LNCS, volume 521)


Uncertainty models play an important role within expert systems. However, there are different types of uncertainty (inaccuracy, inexactitude, fuzziness etc.). It can be shown that the various uncertainty models as known from literature in fact are dealing with different types of uncertainty. The type of uncertainty, which is characteristic for the application for which the expert system is used, has a direct impact on the selection of the appropriate uncertainty model within a given application domain.

Problems appear when within an application domain various types of uncertainty should be handled at the same time (multitype uncertainty). In this case a special inference calculus for e.g. the combination of evidences, related to various types of uncertainty, is needed. In this paper two general methods for an inference calculus for multitype uncertainty will be proposed and evaluated.


Expert systems multitype uncertainty uncertainty classes and types uncertainty calculi inference calculi certainty vector rule inference classification inference rule generation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • J. C. A. van der Lubbe
    • 1
  • E. Backer
    • 1
  • W. Krijgsman
    • 2
  1. 1.Information Theory Group, Dept. of Electrical EngineeringDelft University of TechnologyDelftthe Netherlands
  2. 2.Lab. for Clinical and Experimental Image Processing, ThoraxcenterErasmus University RotterdamRotterdamthe Netherlands

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