Data-Driven Detection of Recursive Program Schemes

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.

Authors

• Martin Hofmann 0008
• Ute Schmid

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