Strongly Typed Inductive Concept LearningP. A. Flach, C. Giraud-Carrier, J. W. Lloyd, Strongly Typed Inductive Concept Learning. 8th International Conference on Inductive Logic Programming (ILP-98). ISBN 3-540-64738-4, pp. 185–194. July 1998. No electronic version available. External information
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.