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 condi-tional higher-order term rewriting as a common framework for inductive logic and inductive functional program synthe-sis. Then we characterise the several ILP systems which be-long either to the most recently researched or currently to the most powerful IP systems within this framework. In conse-quence, 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 evalu-ated and shows the strength of IGOR II