Strongly Typed Inductive Concept Learning

P.A. Flach, C. Giraud-Carrier, and J.W. Lloyd. In D. Page, editor, Proceedings of the 8th International Conference on Inductive Logic Programming, volume 1446 of Lecture Notes in Artificial Intelligence, pages 185--194. Springer-Verlag, 1998. More behind this link.

Abstract

In this paper we argue that the use of a language with a type system, together with higher-order facilities and functions, provides a suitable basis for knowledge representation in inductive concept learning and, in particular, illuminates the relationship between attribute-value learning and inductive logic programming (ILP). Individuals are represented by closed terms: tuples of constants in the case of attribute-value learning; arbitrarily complex terms in the case of ILP. To illustrate the point, we take some learning tasks from the machine learning and ILP literature and represent them in Escher, a typed, higher-order, functional logic programming language being developed at the University of Bristol. We argue that the use of a type system provides better ways to discard meaningless hypotheses on syntactic grounds and encompasses many ad hoc approaches to declarative bias.

BibTeX entry.

Other publications

P A Flach, Peter.Flach@bristol.ac.uk,

C. Giraud-Carrier, cgc@cs.bris.ac.uk,