Abstract
We present two contributions to the explanation-based generalization techniques. First, the operationality criterion is extended by abstraction operators. These allow for the goal concept to be reformulated not only in terms of operational predicates, but also allow to delete irrelevant arguments, and to collapse indistinguishable constants. The abstraction algorithm is presented and illustrated by an example. Second, the domain theory is not restricted to variables with finite (discrete) domains, but can deal with infinite (e.g., real-valued) domains as well. The interpretation and abstraction are effectively handled through constraint logic programming mechanisms. In the paper we concentrate on the role of CLP(ℜ) — a solver for systems of linear equations and inequalities over reals.
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Mozetič, I., Holzbaur, C. (1991). Extending explanation-based generalization by abstraction operators. In: Kodratoff, Y. (eds) Machine Learning — EWSL-91. EWSL 1991. Lecture Notes in Computer Science, vol 482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017021
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DOI: https://doi.org/10.1007/BFb0017021
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