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By Alberto Maria Segre

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Additional resources for Proceedings of the Sixth International Workshop on Machine Learning, Cornell University, Ithaca, New York, June 26-27, 1989

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Since plausible explanation generation does not require all rule antecedents to be satisfied, newly acquired rules may be used in explanations before they are completely rid of irrelevant terms. · Referen ce s Danyluk, A. ( 1989 ) . Finding New Rules for Incomplete Theories: Explicit Biases for Induction with Contex­ tual Information. To appear in Proceedings to the Sizth International Workshop on Machine Learning, Cornell University. Morgan Kaufmann. , and Kedar-Cabelli, S. ( 1 986). Explanation-based generalization: A unifying view.

CON CLUSION In this paper we have discussed a framework for learning k-CNF by combining empirical learning with EBL. This algorithm can easily be extended to include truth maintenance. Finally, although IOSC is limited in applicability by its constrained representation language, k-CNF expressions are powerful enough to be used in describing application domains such as medical diagnoses. References Bruner, J . S . , Goodnow, J . J . , & Austin, G . A. ( 1956) . A Study of Thinking, New York: Wiley.

Although concept definitions consistent with each explanation are considered, the method presented here will drop those generalizations consistent with incorrect explanations if they are not consistent with later data. BIASING SEARCH The alternative way to view this approach to combining empirical and analytical learning is from the perspective of empirical learning, asking what contributions analytical learning makes to empirical learning. The answer is that analytical learning hastens convergence to a final concept definition.

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