This chapter describes a multistrategy system that employs independent modules for deductive, abductive, and inductive reasoning to revise an arbitrarily incorrect propositional Horn-clause domain theory to fit a set of preclassified training instances. By combining such diverse methods, EITHER is able to handle a wider range of imperfect theories than other theory revision systems while guaranteeing that the revised theory will be consistent with the training data. EITHER has successfully revised two actual expert theories, one in molecular biology and one in plant pathology. The results confirm the hypothesis that using a multistrategy system to learn from both theory and data gives better results than using either theory or data alone.

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In Ryszard S. Michalski and G. Teccuci, editors, Machine Learning: A Multistrategy Approach, Vol. IV, 141-164, San Mateo, CA, 1994. Morgan Kaufmann.

@InCollection{mooney:bkchapter94a, title={A Multistrategy Approach to Theory Refinement}, author={Raymond J. Mooney and Dirk Ourston}, booktitle={Machine Learning: A Multistrategy Approach, Vol. IV}, editor={Ryszard S. Michalski and G. Teccuci}, address={San Mateo, CA}, publisher={Morgan Kaufmann}, key={EITHER}, pages={141-164}, url="http://www.cs.utexas.edu/users/ai-lab/?mooney:bkchapter94a", year={1994} }