Evolutionary concept learning in First Order Logic: An overview

Article type: Research Article

Authors: Divina, Federico

Affiliations: Computational Linguistics and AI Section Tilburg University, Tilburg, The Netherlands E-mail: F.Divina@uvt.nl

Abstract: This paper presents an overview of evolutionary approaches to Inductive Logic Programming (ILP). After a short description of the two popular ILP systems FOIL and Progol, we focus on methods based on evolutionary algorithms (EAs). Six systems are described and compared by means of the following aspects: search strategy, representation, hypothesis evaluation, search operators and biases adopted for limiting the hypothesis space. We discuss possible advantages and drawbacks related to the specific features of the systems along these aspects. Issues concerning the relative performance and efficiency of the systems are addressed.

Keywords: Evolutionary computation, Inductive Concept Learning, First Order Logic, Inductive Logic Programming

Journal: AI Communications, vol. 19, no. 1, pp. 13-33, 2006