On the complexity of some inductive logic programming problems
New Generation Computing - Special issue on inductive logic programming 97
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Fast Theta-Subsumption with Constraint Satisfaction Algorithms
Machine Learning
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Constraint models for reasoning on unification in inductive logic programming
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
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Inductive logic programming is a subfield of machine learning that uses first-order logic as a uniform representation for examples and hypothesis. In its core form, it deals with the problem of finding a hypothesis that covers all positive examples and excludes all negative examples. The coverage test and the method to obtain a hypothesis from a given template have been efficiently implemented using constraint satisfaction techniques. In this paper we suggest a method how to efficiently generate the template by remembering a history of generated templates and using this history when adding predicates to a new candidate template. This method significantly outperforms the existing method based on brute-force incremental extension of the template.