Martin Hofmann
ABSTRACT
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.
- M. Hofmann. I/O Guided Detection of List Catamorphisms -- Towards Problem Specific Use of Program Templates in IP. to appear in ACM SIGPLAN 2010 Workshop on Partial Evaluation and Program Manipulation (PEPM'10), 2010. Google Scholar
Digital Library
- S.P. Jones. Haskell 98 Language and Libraries: The Revised Report. Cambridge University Press, May 2003. ISBN 0521826144.Google Scholar
- E. Kitzelmann. Data-driven induction of recursive functions from I/O-examples. In E. Kitzelmann and U. Schmid, editors, Proceedings of the ECML/PKDD 2007 Workshop on Approaches and Applications of Inductive Programming (AAIP'07), pages 15--26, 2007.Google Scholar
- E. Kitzelmann and U. Schmid. Inductive synthesis of functional programs: An explanation based generalization approach. Journal of Machine Learning Research, 7:429--454, 2006. ISSN 1533-7928. Google ScholarDigital Library
- T.M. Mitchell. Machine Learning. McGraw-Hill Higher Education, 1997. ISBN 0070428077. Google ScholarDigital Library
- G. Plotkin. A note on inductive generalisation. In B. Meltzer and D. Michie, editors, Machine Intelligence 5, pages 153--163. Edinburgh University Press, Edinburgh, 1969.Google Scholar
Index Terms
- IGOR2 - an analytical inductive functional programming system: tool demo
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