A Unifying Framework for Analysis and Evaluation of Inductive Programming Systems

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 logic and inductive functional program synthesis. Then we characterise the several ILP systems which belong either to the most recently researched or currently to the most powerful IP systems 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.