New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Complexity and expressive power of logic programming
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Inducing Logic Programs With Genetic Algorithms: The Genetic Logic Programming System
IEEE Expert: Intelligent Systems and Their Applications
Learning Logical Definitions from Relations
Machine Learning
An Experimental Evaluation of Coevolutive Concept Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
On Classification and Regression
DS '98 Proceedings of the First International Conference on Discovery Science
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
DOGMA: A GA-Based Relational Learner
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 Experiment
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Optimal assignment kernels for attributed molecular graphs
ICML '05 Proceedings of the 22nd international conference on Machine learning
Evolutionary concept learning in first order logic: an overview
AI Communications
Randomised restarted search in ILP
Machine Learning
Integrating Naïve Bayes and FOIL
The Journal of Machine Learning Research
Search-intensive concept induction
Evolutionary Computation
A learning classifier system approach to relational reinforcement learning
A learning classifier system approach to relational reinforcement learning
A genetic algorithms approach to ILP
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Iterative prototype optimisation with evolved improvement steps
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Beyond accuracy, f-score and ROC: a family of discriminant measures for performance evaluation
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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We describe a new approach to the application of stochastic search in Inductive Logic Programming (ILP). Unlike traditional approaches we do not focus directly on evolving logical concepts but our refinement-based approach uses the stochastic optimization process to iteratively adapt the initial working concept. Utilization of context-sensitive concept refinements (adaptations) helps the search operations to produce mostly syntactically correct concepts. It also enables using available background knowledge both for efficiently restricting the search space and for directing the search. Thereby, the search is more flexible, less problem-specific and the framework can be easily used with any stochastic search algorithm within ILP domain. Experimental results on several data sets verify the usefulness of this approach.