Learning Horn Expressions with LogAn-H

Abstract

The paper introduces LogAn-H --- a system for learning first-order function-free Horn expressions from interpretations. The system is based on an interactive algorithm (that asks questions) that was proved correct in previous work. The current paper shows how the algorithm can be implemented in a practical system by reducing some ine#ciencies. Moreover, the paper introduces a new algorithm based on it that avoids interaction and learns from examples only. We describe qualitative and quantitative experiments in several domains. The experiments demonstrate that the system can deal with varied problems, large amounts of data and large hypotheses, and that it achieves good classification accuracy. 1. Introduction Work in Inductive Logic Programming (ILP) has established a core set of methods and systems that proved useful in a variety of applications (Muggleton & De Raedt, 1994). Theoretical results, however, identified strong limits to learnability when only examples are...