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Induction and Reasoning from Cases

complement each other. Induction is used for detecting inconsistencies in the case data base, case-based reasoning is used during consulation to retrieve similar cases when there are missing values. The induction system can compute a tree to index cases on a predefined number of levels in order to improve the efficiency of case-based reasoning. After traversing that partial tree (interactive consultation), we are left at a leaf node with an initial candidate set that can be passed to the case-based reasoning system. As a consequence, the case-based reasoner works on a much smaller set of candidates. The partial decisions can be confirmed or refuted by the case-based reasoner. In the latter case the tree needs to be updated.

Acknowledgement
Funding for INRECA has been provided by the Commission of the European Communities (ESPRIT contract P6322). The partners of INRECA are AcknoSoft (prime contractor, France), tecInno (Germany), Irish Medical Systems (Ireland), the University of Kaiserslautern (Germany). KATE is a trademark of Michel Manago. We thank Prof. Claude Lévi and Mr Jacques Le Renard at the Museum of Natural History in Paris for providing the sample application used to illustrate some of the ideas presented here. We also thank Mr Thomas Schultz who has helped us refine our criteria list and who validated and filled our comparison grids for several CBR tools.

References

[1] [2]

Bareiss, R. (1989). Exemplar-Based Knowledge Acquisition. London: Academic Press Quinlan, R. (1983) Learning efficient classification procedures and their application to chess end games. In R. S. Michalski, J. G. Carbonell & T. M. Mitchell (Eds), Machine Learning: An Artificial Intelligence Approach (Vol. 1). Morgan Kaufmann.
Manago M. (1989). "Knowledge Intensive Induction", proceedings of the sixth "International Machine Learning workshop", Morgan Kaufmann.
Althoff, K.-D. & Wess, S. (1991). "Case-Based Knowledge Acquisition, Learning and Problem Solving in Diagnostic Real World Tasks". Proc. EKAW-91, Glasgow & Crieff; also: GMD-Studien Nr. 211 (edited by M. Linster and B. Gaines)
Richter, M. M. & Wess, S. (1991). "Similarity, Uncertainty and Case-Based Reasoning in PATDEX". Automated Reasoning - Essays in Honor of Woody Bledsoe, Kluwer Academic Publishers Quinlan, J. R. (1989). "Unknown Attribute Values in Induction". Proceedings. of the Sixth International Workshop on Machine Learning, pp. 164-168,. Morgan-Kaufmann.

[3]

[4]

[5]

[6]