Information Processing Letters
Logical settings for concept-learning
Artificial Intelligence
On the complexity of some inductive logic programming problems
New Generation Computing - Special issue on inductive logic programming 97
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Relational Data Mining
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
Eighteenth national conference on Artificial intelligence
Fast Theta-Subsumption with Constraint Satisfaction Algorithms
Machine Learning
Propositionalization-based relational subgroup discovery with RSD
Machine Learning
Randomised restarted search in ILP
Machine Learning
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Fast estimation of first-order clause coverage through randomization and maximum likelihood
Proceedings of the 25th international conference on Machine learning
Undecidability of the Horn-clause implication problem
SFCS '92 Proceedings of the 33rd Annual Symposium on Foundations of Computer Science
A Restarted Strategy for Efficient Subsumption Testing
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Lattice-search runtime distributions may be heavy-tailed
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Probabilistic inductive logic programming: theory and applications
Probabilistic inductive logic programming: theory and applications
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Inductive logic programming (ILP) [12] is concerned with the induction of theories from specific examples and background knowledge, using first-order logic representations for all the three ingredients. In its early days some twenty years ago, ILP was perceived as a means for automatic synthesis of logic programs, i.e. Horn clausal theories. Current research views ILP algorithms mainly in the context of machine learning [14] and data mining [1]. ILP has enriched both of the two fieds significantly by providing them with formalisms and algorithms for learning (or `mining') complex pieces of knowledge from non-trivially structured data such as relational databases.