Existence and nonexistence of complete refinement operators
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Machine Learning - special issue on inductive logic programming
Logical settings for concept-learning
Artificial Intelligence
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
A Refinement Operator for Description Logics
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
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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 specializtion rather than a generalization. In this article we define substitutions to specialize 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.