Generalizing Refinement Operators to Learn Prenex Conjunctive Normal Forms | Zendy

Shan-Hwei Nienhuys-Cheng | Zendy; Wim Van Laer | Zendy; Jan Ramon | Zendy; Luc De Raedt | Zendy

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Generalizing Refinement Operators to Learn Prenex Conjunctive Normal Forms

Author(s) -

Shan-Hwei Nienhuys-Cheng,

Wim Van Laer,

Jan Ramon,

Luc De Raedt

Publication year - 1999

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-66109-3

DOI - 10.1007/3-540-48751-4_23

Subject(s) - generalization , computer science , operator (biology) , inductive logic programming , simple (philosophy) , algorithm , theoretical computer science , artificial intelligence , algebra over a field , mathematics , pure mathematics , mathematical analysis , biochemistry , chemistry , philosophy , epistemology , repressor , transcription factor , gene

Inductive Logic Programming considers almost exclusively universally quantified theories. To add expressiveness we should consider general prenex conjunctive normal forms (PCNF) with existential variables. ILP mostly uses learning with refinement operators. To extend refinement operators to PCNF, we should first extend substitutions to PCNF. If one substitutes an existential variable in a formula, one often obtains a specialization rather than a generalization. In this article we define substitutions to specialise a given PCNF and a weakly complete downward refinement operator. Based on this operator, we have implemented a simple learning system PCL on some type of PCNF.status: publishe

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