Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Beyond inversion of resolution
Proceedings of the seventh international conference (1990) 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
Confirmation-guided discovery of first-order rules with tertius
Machine Learning
Integrating explanatory and descriptive learning in ILP
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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Traditional Machine Learning approaches are based on single inference mechanisms. A step forward concerned the integration of multiple inference strategies within a first-order logic learning framework, taking advantage of the benefits that each approach can bring. Specifically, abduction is exploited to complete the incoming information in order to handle cases of missing knowledge, and abstraction is exploited to eliminate superfluous details that can affect the performance of a learning system. However, these methods require some background information to exploit the specific inference strategy, that must be provided by a domain expert. This work proposes algorithms to automatically discover such an information in order to make the learning task completely autonomous. The proposed methods have been tested on the system INTHELEX, and their effectiveness has been proven by experiments in a real-world domain.