ECML '93 Proceedings of the European Conference on Machine Learning
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Relational Learning: Hard Problems and Phase Transitions
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Relational learning as search in a critical region
The Journal of Machine Learning Research
Integrating top-down and bottom-up approaches in inductive logic programming: applications in natural language processing and relational data mining
Extension of the Top-Down Data-Driven Strategy to ILP
Inductive Logic Programming
A Model to Study Phase Transition and Plateaus in Relational Learning
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Empirical Study of Relational Learning Algorithms in the Phase Transition Framework
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Learning on the phase transition edge
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
On Learning Constraint Problems
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 01
| Hi-index | 0.00 |
It is well known that for certain relational learning problems, traditional top-down search falls into blind search. Recent works in Inductive Logic Programming about phase transition and crossing plateau show that no general solution can face to all these difficulties. In this context, we introduce the notion of "minimal saturation" to build nonblind refinements of hypotheses in a bidirectional approach. We present experimental results of this approach on some benchmarks inspired by constraint satisfaction problems. These problems can be specified in first order logic but most existing ILP systems fail to learn a correct definition, especially because they fall into blind search.