Abduction and Learning

In this paper we study the problem of of integrating abduction and learning as they appear in Arti cial Intelligence. A general comparison of abduction and induction as separate inferences and elds in AI is given. Based on this analysis we study their possible interaction and integration. We introduce the notions of of abductive concept learning as a framework for learning with incomplete background theories. Together with this we propose a general methodology for incorporating abduction in inductive concept learning that allows us to exploit more fully in the learning process the knowledge contained in the background theory. This basic methodology extends in a natural way to the more general context of theory revision.