Machine Learning
Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach
The Journal of Machine Learning Research
I/O guided detection of list catamorphisms: towards problem specific use of program templates in IP
Proceedings of the 2010 ACM SIGPLAN workshop on Partial evaluation and program manipulation
Concept of inductive programming supporting anthropomorphic information technology
Journal of Computer and Systems Sciences International
Ideas for connecting inductive program synthesis and bidirectionalization
PEPM '12 Proceedings of the ACM SIGPLAN 2012 workshop on Partial evaluation and program manipulation
Synthesis modulo recursive functions
Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications
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The analytical inductive programming system IGOR II is an implemented prototype for constructing recursive functional programs from few non-recursive, possibly non-ground example equations describing a subset of the input/output (I/O) behaviour of a function. Starting from an initial, overly general program hypothesis, stepwise several refinement operators are applied which compute successor hypotheses. Organised as an uniformed-cost search, the hypothesis with the lowest costs is developed and replaced by its successors until the best does not contain any unbound variables.