Theory of generalized annotated logic programming and its applications
Journal of Logic Programming
Learning structured concepts using genetic algorithms
ML92 Proceedings of the ninth international workshop on Machine learning
Inductive learning of characteristic concept descriptions from small sets of classified examples
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Recovering software specifications with inductive logic programming
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Applications of inductive logic programming
Communications of the ACM
Machine Learning
Machine Learning - special issue on inductive logic programming
Machine Learning - special issue on inductive logic programming
Learning Qualitative Models of Dynamic Systems
Machine Learning - special issue on inductive logic programming
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Inductive logic programming for relational knowledge discovery
New Generation Computing - Special issue on inductive logic programming 97
Strategies in Combined Learning via Logic Programs
Machine Learning - Special issue on multistrategy learning
Advances in Inductive Logic Programming
Advances in Inductive Logic Programming
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Learning Logical Definitions from Relations
Machine Learning
Learning Nonrecursive Definitions of Relations with LINUS
EWSL '91 Proceedings of the European Working Session on Machine Learning
Predicate Invention in Inductive Data Engineering
ECML '93 Proceedings of the European Conference on Machine Learning
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Using Logical Decision Trees for Clustering
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Relational Distance-Based Clustering
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Relational Reinforcement Learning
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Strongly Typed Inductive Concept Learning
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
An Inductive Logic Programming Query Language for Database Mining
AISC '98 Proceedings of the International Conference on Artificial Intelligence and Symbolic Computation
Learning Non-Monotonic Logic Programs: Learning Exceptions
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Cooking up integrity constraints with PRIMUS (preliminary report)
Cooking up integrity constraints with PRIMUS (preliminary report)
Integrating explanatory and descriptive learning in ILP
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Induction of first-order decision lists: results on learning the past tense of English verbs
Journal of Artificial Intelligence Research
Well-founded semantics for extended logic programs with dynamic preferences
Journal of Artificial Intelligence Research
The predictive toxicology evaluation challenge
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Inductive logic programming (ILP) is a research area that has its roots in inductive machine learning and logic programming. Computational logic has significantly influenced machine learning through the field of inductive logic programming (ILP) which is concerned with the induction of logic programs from examples and background knowledge. Machine learning, and ILP in particular, has the potential to influence computational logic by providing an application area full of industrially significant problems, thus providing a challenge for other techniques in computational logic. In ILP, the recent shift of attention from program synthesis to knowledge discovery resulted in advanced techniques that are practically applicable for discovering knowledge in relational databases. This paper gives a brief introduction to ILP, presents state-of-the-art ILP techniques for relational knowledge discovery as well as some challegnes and directions for further developments in this area.