Machine Learning from Examples: A Non-Inductivist Analysis

It has been suggested that AI investigations of mechan- ical learning undermine sweeping anti-inductivist views in the theory of knowledge and the philosophy of science. In particular, it is claimed that some mechanical learning systems perform epistemically justified inductive generalization and prediction. Contrary to this view, it is ar- gued that no trace of such epistemic justification is to be found within a rather representative class of learning agents drawn from machine learning and robotics. Moreover, an alternative deductive account of these learning procedures is outlined. Finally, the opportunity of de- veloping an induction-free logical analysis of non-monotonic reason- ing in autonomous learning agents - capable of advancing and revising learning or background hypotheses - is emphasized by a broad reflec- tion on some families of non-monotonic, albeit deductive, consequence relations. 1