A logical framework for default reasoning
Artificial Intelligence
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
Machine Learning - special issue on inductive logic programming
Multistrategy Theory Revision: Induction and Abductionin INTHELEX
Machine Learning - Special issue on multistrategy learning
Machine Learning for Intelligent Processing of Printed Documents
Journal of Intelligent Information Systems - Special issue on methodologies for intelligent information systems
New Generation Computing
Confirmation-guided discovery of first-order rules with tertius
Machine Learning
Parallel and sequential algorithms for data mining using inductive logic
Knowledge and Information Systems
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Knowledge discovery with second-order relations
Knowledge and Information Systems
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Combining Concept Maps and Petri Nets to Generate Intelligent Tutoring Systems: A Possible Approach
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
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In real-life domains, learning systems often have to deal with various kinds of imperfections in data such as noise, incompleteness and inexactness. This problem seriously affects the knowledge discovery process, specifically in the case of traditional Machine Learning approaches that exploit simple or constrained knowledge representations and are based on single inference mechanisms. Indeed, this limits their capability of discovering fundamental knowledge in those situations. In order to broaden the investigation and the applicability of machine learning schemes in such particular situations, it is necessary to move on to more expressive representations which require more complex inference mechanisms. However, the applicability of such new and complex inference mechanisms, such as abductive reasoning, strongly relies on a deep background knowledge about the specific application domain. This work aims at automatically discovering the meta-knowledge needed to abduction inference strategy to complete the incoming information in order to handle cases of missing knowledge.