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. 1. Motivation and scope Inductive concept learning consists of finding mappings of individuals (or objects...

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