Evidential Reasoning Under Probabilistic and Fuzzy Uncertainties

  • J. F. Baldwin
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 165)


An expert’s knowledge of an application is concerned with general tendencies, what is likely to be the case, frequent conjunctions, rules of thumb and other forms of statistical statements. An investigator may know that a certain type of crime is common among criminals of a certain type, an insurance company may know that a person with certain characteristics is a good risk, a doctor knows that certain symptoms almost always means the person is suffering from a given disease. The conclusion in each of these cases comes from studying tendencies in a population of relevant cases and using these to infer something about an individual case.


Support Measure Assignment Method Possibility Distribution Focal Element Specific Evidence 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Baldwin J.F, (1986), “Support Logic Programming”, in: A.I. Jones et al,. Eds., Fuzzy Sets Theory and Applications, (Reidel, Dordrecht-Boston).Google Scholar
  2. Baldwin J.F, (1987), “Evidential Support Logic Programming”, Fuzzy Sets and Systems, 24, pp 1–26.MATHCrossRefGoogle Scholar
  3. Baldwin J.F. et al, (1987), “FRIL Manual”, Fril Systems Ltd, St Anne’s House, St Anne’s Rd, Bristol BS4 4A, UKGoogle Scholar
  4. Baldwin J.F., (1990a), “Computational Models of Uncertainty Reasoning in Expert Systems”, Computers Math. Applic., Vol. 19, No 11, pp 105–119.MATHCrossRefGoogle Scholar
  5. Baldwin J.F., (1990b), “Combining Evidences for Evidential Reasoning”, Int. J. of Intelligent Systems, To Appear.Google Scholar
  6. Baldwin J.F., (1990c), “Towards a general theory of intelligent reasoning”, 3rd Int. Conf IPMU, Paris, July 1990Google Scholar
  7. Dubois D., Prade H., (1986), “On the unicity of Dempster Rule of Combination”, Int. J. of Intelligent Systems, 1, no. 2, pp 133–142MATHCrossRefGoogle Scholar
  8. Jeffrey R., (1965) “The Logic of Decision”, McGraw-Hill, New YorkCrossRefGoogle Scholar
  9. Klir GJ., Folger T.A., (1988), Fuzzy Sets, Uncertainty, and Information, Prentice-MATHCrossRefGoogle Scholar
  10. Lauritzen S.L, Spiegelhalter DJ., (1988), Local computations with probabilities on graphical structures and their application to expert systems, J. Roy. Stat. Soc. Ser. B 50(2), 157–224MathSciNetMATHGoogle Scholar
  11. Pearl J., (1988), “Probabilistic reasoning in Intelligent Systems”, Morgan Kaufmann Pub. Co.Google Scholar
  12. Shafer G., (1976), “A mathematical theory of evidence”, Princeton Univ. PressGoogle Scholar
  13. Shenoy P.P., (1989), A Valuation - Based Language for Expert Systems, Int. J. of Approx. Reasoning, V013, No. 5.Google Scholar
  14. Zadeh L, (1965), “Fuzzy sets”, Information and Control, 8, pp 338–353.MathSciNetMATHCrossRefGoogle Scholar
  15. Zadeh L, (1978), “Fuzzy Sets as a basis for a theory of Possibility”, Fuzy Sets and Systems 1, 3–28MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • J. F. Baldwin
    • 1
  1. 1.Engineering Mathematics DeptUniversity of BristolBristolEngland

Personalised recommendations