Abstract
The purpose of this paper is to present a theory and an algorithm for analogical logic program synthesis from examples. Given a source program and examples, the task of our algorithm is to find a program which explains the examples correctly and is similar to the source program. Although we can define a notion of similarity in various ways, we consider a class of similarities from the viewpoint of how examples are explained by a program. In a word, two programs are said to be similar if they share a common explanation structure at an abstract level. Using this notion of similarity, we formalize an analogical logic program synthesis and show that our algorithm based on a framework of model inference can identify a desired program.
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© 1995 Springer-Verlag Berlin Heidelberg
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Sadohara, K., Haraguchi, M. (1995). Analogical logic program synthesis from examples. In: Lavrac, N., Wrobel, S. (eds) Machine Learning: ECML-95. ECML 1995. Lecture Notes in Computer Science, vol 912. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59286-5_61
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DOI: https://doi.org/10.1007/3-540-59286-5_61
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