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Generalizing Refinement Operators to Learn Prenex Conjunctive Normal Forms

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Author Info
Nienhuys-Cheng, S-H.
Laer, W. van
Ramon, J.
Raedt, L. de (Erasmus Research Institute of Management (ERIM), RSM Erasmus University)
Abstract

Inductive Logic Programming considers almost exclusively universally quantied theories. To add expressiveness, prenex conjunctive normal forms (PCNF) with existential variables should also be considered. ILP mostly uses learning with refinement operators. To extend refinement operators to PCNF, we should first do so with substitutions. However, applying a classic substitution to a PCNF with existential variables, one often obtains a generalization rather than a specialization. In this article we define substitutions that specialize a given PCNF and a weakly complete downward refinement operator. Moreover, we analyze the complexities of this operator in different types of languages and search spaces. In this way we lay a foundation for learning systems on PCNF. Based on this operator, we have implemented a simple learning system PCL on some type of PCNF.

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Publisher Info
Paper provided by Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam. in its series Research Paper with number ERS-2000-39-LIS Revision_Date: 2008-03-04.

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Date of creation: 16 Nov 2000
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Handle: RePEc:dgr:eureri:200049

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Related research
Keywords: PCNF; substitutions; refinement; learning; completeness;

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