Abstract
In this paper we investigate generalization methods in typed equational programming and apply them to inductive inference of functions. We are interested in inducing programs from given examples which are input-output pairs. Our main contribution is a new generalization algorithm which uses type polymorphism. With the algorithm we introduce, for the first time, a generalization phase to Summers’ method. Moreover, we present a new bottom-up inference method which combines elements of the generalization algorithm, a minimal multiple generalization algorithm, and Summers’ method. This integration is enabled with the adaptation of equational programming.
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Akira Ishino: He received the B. E. and M. E. degrees from Hokkaido University in 1993 and 1995, respectively. He is now a student in the doctor course of the same university. His research interest in machine learning include automatic programming for recursive programs founded on equational programming and type systems.
Akihiro Yamamoto: He is an Associate Professor of Division of Electronics and Information Engineering at Hokkaido University. He received the B. S. degree from Kyoto University in 1985, and the M. S. and Dr. Sci. degrees from Kyusyu University in 1987 and 1990, respectively. His present interests include unification theory, types in logic programming, and their application to inductive inference. He is now a guest researcher of Computer Science Department at Technical University Darmstadt, Germany.
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Ishino, A., Yamamoto, A. Generalizations in type equational programming and their application to learning functions. New Gener Comput 15, 85–103 (1997). https://doi.org/10.1007/BF03037561
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DOI: https://doi.org/10.1007/BF03037561