Abstract

FOIL is a first-order learning system that uses information in a collection of relations to construct theories expressed in a dialect of Prolog. This paper provides an overview of the principal ideas and methods used in the current version of the system, including two recent additions. We present examples of tasks tackled by FOIL and of systems that adapt and extend its approach. 1. Introduction All symbolic machine learning leads to the formulation or modification of theories, so the language in which theories are expressed is an important consideration. Firstorder theory languages have been used for at least thirty years, as documented by Sammut [1993]. Explanation-based generalisation systems [Mitchell, Keller and Kedar-Cabelli, 1986; DeJong and Mooney, 1986] have always required them, but the early and influential work of Shapiro [1983] and Sammut and Banerji [1986] also employed them in an inductive learning context. Nevertheless, first-order empirical learning, including...

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