This article discusses the integration of traditional abductive and inductive reasoning methods in the development of machine learning systems. In particular, it reviews 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. Experimental results on real-world problems are presented to illustrate the capabilities of both of these approaches to integrating the two forms of reasoning.

PDF, PS

In Abduction and Induction, P. A. Flach and A. C. Kakas (Eds.), pp. 181-191 2000. Kluwer Academic Publishers.

@InCollection{mooney:bkchapter00, title={Integrating Abduction and Induction in Machine Learning}, author={Raymond J. Mooney}, booktitle={Abduction and Induction}, editor={P. A. Flach and A. C. Kakas}, publisher={Kluwer Academic Publishers}, key={abind}, pages={181-191}, url="http://www.cs.utexas.edu/users/ai-lab?mooney:bkchapter00", year={2000} }