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

Raymond J. Mooney Faculty mooney [at] cs utexas edu