Abstract

First-order learning involves finding a clause-form definition of a relation from examples of the relation and relevant background information. In this paper, a particular first-order learning system is modified to customize it for finding definitions of functional relations. This restriction leads to faster learning times and, in some cases, to definitions that have higher predictive accuracy. Other first-order learning systems might benefit from similar specialization. 1. Introduction Empirical learning is the subfield of AI that develops algorithms for constructing theories from data. Most classification research in this area has used the attribute-value formalism, in which data are represented as vectors of values of a fixed set of attributes and are labelled with one of a small number of discrete classes. A learning system then develops a mapping from attribute values to classes that can be used to classify unseen data. Despite the well-documented successes of algorithms develope...

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