Analysis and Evaluation of Inductive Programming Systems in a Higher-Order Framework

Abstract. In this paper we present a comparison of several inductive programming (IP) systems. IP addresses the problem of learning (recursive) programs from incomplete specifications, such as input/output examples. First, we introduce conditional higher-order term rewriting as a common framework for inductive program synthesis. Then we characterise the ILP system GOLEM and the inductive functional system MAGICHASKELLER within this framework. In consequence, we propose the inductive functional system IGOR II as a powerful and efficient approach to IP. Performance of all systems on a representative set of sample problems is evaluated and shows the strength of IGOR II.

• Type: Journal
• Year: 2008
• Where: KI
• Authors: Martin Hofmann 0008, Emanuel Kitzelmann, Ute Schmid
• Keywords: Artificial Intelligence, IGOR II, Inductive Program Synthesis, Inductive Programming, KI 2008