This paper discusses the integration of traditional abductive and inductive reasoning methods in the development of machine learning systems. In particular, the paper discusses our recent work in two areas: 1) The use of traditional abductive methods to propose revisions during theory refinement, where an existing knowledge base is modified to make it consistent with a set of empirical data; and 2) The use of inductive learning methods to automatically acquire from examples a diagnostic knowledge base used for abductive reasoning.

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In Working Notes of the IJCAI-97 Workshop on Abduction and Induction in AI, pp. 37--42, Nagoya, Japan, August 1997.

@inproceedings{mooney:ijcai-abin97, title={Integrating Abduction and Induction in Machine Learning}, author={Raymond J. Mooney}, booktitle={Working Notes of the IJCAI-97 Workshop on Abduction and Induction in AI}, month={August}, address={Nagoya, Japan}, pages={37--42}, url="http://www.cs.utexas.edu/users/ai-lab?mooney:ijcai-abin97", year={1997} }