Case based reasoning, fuzzy systems modeling and solution composition

  • Ronald R. Yager
Scientific Papers CBR And Uncertainty
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1266)


Fuzzy systems modeling technique and the case based reasoning methodology are briefly described. It is then shown that these two approaches can be viewed as essentially involving the same process, a matching step and a solution composition step. It is noted that in the typical case based reasoning application the solution composition step is more difficult. Two techniques are suggested to help in the solution composition task in case based reasoning. The first, the weighted median, is useful in domains in which the action space consists of an ordered collection of alternatives. The second, a variation of reinforcement learning, is useful in domains in which the resulting actions involve a sequence of steps.


Fuzzy modeling reinforcement learning matching solution composition 


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  1. [1].
    Yager, R. R. and Filev, D. P., Essentials of Fuzzy Modeling and Control, John Wiley: New York, 1994.Google Scholar
  2. [2].
    Kolodner, J., Case-Based Reasoning, Morgan Kaufmann: San Mateo, CA, 1993.Google Scholar
  3. [3].
    Yager, R. R., “Information fusion and weighted median aggregation,” Proceedings 5th International CIFT Workshop Trento, Italy, 209–219, 1995.Google Scholar
  4. [4].
    Yager, R. R., “Fusion of ordinal information using weighted median aggregation,” Technical Report# MII-1520 Machine Intelligence Institute, Iona College, 1995.Google Scholar
  5. [5].
    Yager, R. R. and Rybalov, A., “Understanding the Median as a Fusion Operator,” International Journal of General Systems, (To Appear).Google Scholar
  6. [6].
    Barto, A. G., Sutton, R. S. and Anderson, C. W., “Neuronlike adaptive elements that can solve difficult learning control problems,” IEEE Transactions on Systems, Man and Cybernetics 13, 834–846, 1983.Google Scholar
  7. [7].
    Zadeh, L. A., “Similarity relations and fuzzy orderings,” Inf. Sci. 3, 177–200, 1971.Google Scholar
  8. [8].
    Dubois, D. and Prade, H., “A review of fuzzy sets aggregation connectives,” Information Sciences 36, 85–121, 1985.Google Scholar
  9. [9].
    Zadeh, L. A., “Fuzzy sets and information granularity,” in Advances in Fuzzy Set Theory and Applications, Gupta, M.M., Ragade, R.K. & Yager, R.R. (eds.), Amsterdam: North-Holland, 3–18, 1979.Google Scholar
  10. [10].
    Zadeh, L. A., “A computational approach to fuzzy quantifiers in natural languages,” Computing and Mathematics with Applications 9, 149–184, 1983.Google Scholar
  11. [11].
    Yager, R. R., “Quantifier guided aggregation using OWA operators,” International Journal of Intelligent Systems 11, 49–73, 1996.Google Scholar
  12. [12].
    Yager, R. R., “On ordered weighted averaging aggregation operators in multi-criteria decision making,” IEEE Trans. on Sys, Man and Cyber. 18, 183–190, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Ronald R. Yager
    • 1
  1. 1.Machine Intelligence InstituteIona CollegeNew Rochelle

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