Case Based Reasoning, Fuzzy Systems Modeling and
Solution Composition


Ronald R. Yager
Machine Intelligence Institute, Iona College
New Rochelle, NY 10801


Abstract
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.

References
[1]. Yager, R. R. and Filev, D. P., Essentials of Fuzzy Modeling and Control, John
Wiley: New York, 1994.
[2]. Kolodner, J., Case-Based Reasoning, Morgan Kaufmann: San Mateo, CA, 1993.
[3]. Yager, R. R., Information fusion and weighted median aggregation, Proceedings
5th International CIFT Workshop Trento, Italy, 209-219, 1995.
[4]. Yager, R. R., Fusion of ordinal information using weighted median aggregation,
Technical Report# MII-1520 Machine Intelligence Institute, Lona College, 1995.
[5]. Yager, R. R. and Rybalov, A., Understanding the Median as a Fusion Operator,
International Journal of General Systems, (To Appear).
[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.
[7]. Zadeh, L. A., Similarity relations and fuzzy orderings, Inf. Sci. 3, 177-200,
1971.
[8]. Dubois, D. and Prade, H., A review of fuzzy sets aggregation connectives,
Information Sciences 36, 85 - 121, 1985.
[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.
[10]. Zadeh, L. A., A computational approach to fuzzy quantifiers in natural
languages, Computing and Mathematics with Applications 9, 149-184, 1983.
[11]. Yager, R. R., Quantifier guided aggregation using OWA operators,
International Journal of Intelligent Systems 11, 49-73, 1996.
[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.
