Non-monotonic characterization of induction and its application to inductive learning
Gustavo Núñez, Ulises Cortés, Javier Larrosa · International Journal of Intelligent Systems · 1995
In this article a new approach to the formalization of inductive inference in terms of non-monotonic inference is proposed. Induction is characterized as closed-world reasoning from the available data, followed by an inductive jump, which consists in assuming that valid conclusions in the database (assuming closed-world) hold also in the rest of the world. This conception of induction results is adequate to characterize those inference processes that could be formalized, that is, those based in analytical procedures of pattern-matching or regularity detection in the available data. the proposed characterization formally describes the implicit deductive processes of induction and its non-monotonic nature, and could be used as an abstract model of the mental process that leads to obtaining inductive hypotheses. This proposal reduces the problem of induction automatization to that of deduction automatization. Also, it constitutes a formal framework that covers several inductive inference methods used in machine learning. Besides it formalizes inductive definitions, which are very common in science and computer science. © 1995 John Wiley & Sons, Inc.