Abstract
Two representation changes are presented: the first one, called flattening, transforms a first-order logic program with function symbols into an equivalent logic program without function symbols; the second one, called saturation, completes an example description with relevant information with respect to both the example and available background knowledge. The properties of these two represenlation changes are analyzed as well as their influence on a generalization algorithm that takes a single example as input.
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Rouveirol, C. Flattening and Saturation: Two Representation Changes for Generalization. Machine Learning 14, 219–232 (1994). https://doi.org/10.1023/A:1022678217288
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DOI: https://doi.org/10.1023/A:1022678217288