Computing Uncertainty in Interval Based Sets

  • Luis Mateus Rocha
  • Vladik Kreinovich
  • R. Baker Kearfott
Part of the Applied Optimization book series (APOP, volume 3)


The kinds of uncertainty present in different interval based techniques of representing uncertainty in knowledge-based systems will be discussed. Interval-Valued Fuzzy Sets (IVFS) will be shown to describe both fuzziness and nonspecificity in their membership degrees, while a structure called an evidence set further introduces conflict. A more realistic model of uncertainty is described by L-fuzzy sets, interval-valued L-fuzzy sets, and L-evidence sets, where L is a finite set of possible degrees of confidence. Measures of uncertainty of such structures are examined.


Membership Function Subjective Probability Membership Degree Fuzzy Measure Focal Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Luis Mateus Rocha
    • 1
  • Vladik Kreinovich
    • 2
  • R. Baker Kearfott
    • 3
  1. 1.Department of Systems Science and Industrial Engineering, T.J. Watson SchoolState University of New York at BinghamtonBinghamtonUSA
  2. 2.Department of Computer ScienceUniversity of Texas at El PasoEl PasoUSA
  3. 3.Department of MathematicsUniversity of Southwestern Louisiana, U.S.LLafayetteUSA

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