A Probabilistic Model for Case-Based Reasoning


Andrs F. Rodrguez1, Sunil Vadera1 and L. Enrique Sucar2

1 University of Salford
Department of Mathematics and Computer Science
Salford, M5 4WT,
{A.Rodriguez / S.Vadera}@cms.salford.ac.uk
2 ITESM - Campus Morelos
AP C-99, Cuernavaca, Mor., 62020, Mexico
esucar@campus.mor.itesm.mx


Abstract. An exemplar-based model with foundations in Bayesian networks 
is described. The proposed model utilises two Bayesian networks:
one for indexing of categories, and another for identifying exemplars
within categories. Learning is incrementally conducted each time a new
case is classified. The representation structure dynamically changes each
time a new case is classified and a coverage function is used as a basis
for selecting suitable exemplars. Finally, a simple example is given to
illustrate the concepts in the model.
References

1.	D.W. Aha. Case-based learning algorithms. In Proc. of the DARPA Case-Based
Reasoning Workshop, pages 147158, Washington, D.C.: Morgan Kaufmann, 1991.
2.	D.W. Aha and L.W. Chang. Cooperative bayesian and case-based reasoning for
solving multiagent planning tasks. Technical Report AIC-96-005, Navy Center
for Applied Research in Al Naval Research Laboratory, Washington, DC, U.S.A.,
1996.
3.	R. Bareiss. Exemplar-based knowledge acquisition. A unified approach to concept
representation, classification, and learning. Academic Press Inc., Harcourt Brace
Jovanovich Publishers, San Diego, Calif., U.S.A., 1989.
4.	H.W. Beck, T. Anwar, and S.B. Navathe. A conceptual clustering algorithm for
database schema design. IEEE Transaction on Knowledge and Data Engineering,
6(3):396411, 1994.
5.	Y. Biberman. The role of prototypicality in exemplar-based learning. In Nada
Lavrac and Stefan Wrobel, editors, Proc. Machine Learning: ECML-95, 8th Eu-
ropean Conference on Machine Learning, pages 7791, Heraclion, Crete, Greece,
1995.
6.	J.S. Breese and D. Heckerman. Decision-theoretic case-based reasoning. In Proc.
of the Fifth International Workshop on Artificial Intelligence and Statistics, pages
5663, Ft. Lauderdale, U.S.A., 1995.
7.	L. Chang and P. Harrison. A case-based reasoning testbed for experiments in
adaptive memory retrieval and indexing. In D.H. Aha and A. Ram, editors, Proc.
of the AAAI fall Symposium on Adaptation of Knowledge for Reuse, Menlo Park:
AAAI Press., 1995.
8.	T. Dean, J. Allen, and Y. Aloimonos. Artificial Intelligence theory and practice.
The Benjamin/Cummings Publishing Company, Inc., Redwood City, Calif., U.S.A.,
1995.
9.	D.L. Medin and M.M. Schaffer. Contex theory of clasification learning. Psychological 
Review, 85:207238, 1978.
10.	S. Minton, J.G. Carbonell, C.A. Knoblock, D.R. Kuokka, 0. Etzioni, and Y. Gil.
Explanation-based learning: a problem solving perspective. In Jaime Carbonell,
editor, Machine Learning: Paradigms and Methods, pages 63118. MIT/Elsevier
Science, Cambridge, Massachusetts, U.S.A., 1990.
11.	P. Myllymki and H. Tirri. Massively parallel case-based reasoning with probabilistic 
similarity metrics. In Klauss-Dieter Althoff Stefan Wess and Michael M.
Ritcher, editors, Topics in Case-Based Reasoning, pages 144154. Volume 837,
Lecture Notes in Artificial Intelligence. Springer Verlag, 1994.
12.	J. Pearl. Probabilistic reasoning in intelligent systems. Morgan Kaufmann, Palo
Alto, Calif., U.S.A., 1988.
13.	B.W. Porter, R. Bareiss, and R.C. Holte. Concept learning and heuristic classification 
in weak-theory domains. Artificial Intelligence, University of Texas, Austin
Texas, U.S.A., (45):229263, 1990.
14.	E. Rosch and C.B. Mervis. Family resemblance studies in the internal structure of
categories. Cognitive Psychology, (7):573605, 1975.
15.	E. Smith and D. Medin. Categories and concepts. Cambride: Harvard University
Press, U.S.A., 1981.
16.	H. Tirri, P. Kontkanen, and P. Myllymki. A bayesian framework for case-based
reasoning. In Proc. of the 3rd European Workshop on Case-based Reasoning, Lansanne, Switzerland, 1996.
