Data-Driven Detection of Recursive Program Schemes
Martin O. Hofmann, Ute Schmid · Frontiers in artificial intelligence and applications · 2010
We present an extension to a current approach to inductive programming (IGOR2), that is, learning (recursive) programs from incomplete specifications such as input/outout examples. IGOR2 uses an analytical, example-driven strategy for generalization. We extend the set of IGOR2's refinement operators by a further operator – identification of higher-order schemes – and can show that this extension does improve speed as well as scope.