Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Logic programming and databases
Logic programming and databases
Beyond inversion of resolution
Proceedings of the seventh international conference (1990) on Machine learning
Artificial Intelligence
Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
Equality and Domain Closure in First-Order Databases
Journal of the ACM (JACM)
Learning Function-Free Horn Expressions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Multistrategy Theory Revision: Induction and Abductionin INTHELEX
Machine Learning - Special issue on multistrategy learning
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Relational Data Mining
Propositionalization approaches to relational data mining
Relational Data Mining
Abstracting Visual Percepts to Learn Concepts
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Perceptual Learning and Abstraction in Machine Learning
ICCI '03 Proceedings of the 2nd IEEE International Conference on Cognitive Informatics
Propositionalization for clustering symbolic relational descriptions
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
A Generalization Model Based on OI-implication for Ideal heory Refinement
Fundamenta Informaticae - Intelligent Systems
ICCS '07 Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications
25 years of applications of logic programming in Italy
A 25-year perspective on logic programming
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Traditional Machine Learning approaches based on single inference mechanisms have reached their limits. This causes the need for a framework that integrates approaches based on abduction and abstraction capabilities in the inductive learning paradigm, in the light of Michalski's Inferential Theory of Learning (ITL). This work is intended as a survey of the most significant contributions that are present in the literature, concerning single reasoning strategies and practical ways for bringing them together and making them cooperate in order to improve the effectiveness and efficiency of the learning process. The elicited role of an abductive proof procedure is tackling the problem of incomplete relevance in the incoming examples. Moreover, the employment of abstraction operators based on (direct and inverse) resolution to reduce the complexity of the learning problem is discussed. Lastly, a case study that implements the combined framework into a real multistrategy learning system is briefly presented.