New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Strategies in Combined Learning via Logic Programs
Machine Learning - Special issue on multistrategy learning
Advances in Inductive Logic Programming
Advances in Inductive Logic Programming
Algorithmic Program DeBugging
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Extending and implementing the stable model semantics
Artificial Intelligence
Learning Logical Definitions from Relations
Machine Learning
Normal Programs and Multiple Predicate Learning
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Learning Non-Monotonic Logic Programs: Learning Exceptions
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Ilp: a short look back and a longer look forward
The Journal of Machine Learning Research
Induction from answer sets in nonmonotonic logic programs
ACM Transactions on Computational Logic (TOCL)
Learning extended logic programs
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Inductive logic programming by instance patterns
PADL'07 Proceedings of the 9th international conference on Practical Aspects of Declarative Languages
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In this paper, we present a new approach, called NM-ILP-IP, for inductive learning in the context of nonmonotonic logic frameworks. This approach is based on the notations of concept instances and instance patterns introduced in [13]. When a strictly correct Horn theory cannot be induced, this approach induces a normal logic program, by specializing a previously learned overly-general theory. The advantages of this approach over others include: (a) it does not rely on existing ILP systems, and it avoids many of the effectiveness and efficiency drawbacks of ordinary ILP systems; (b) no theorem prover is needed during the learning process; (c) it introduces negation as failure (NAF) of existing predicates and introduces new abnormality predicates only when necessary, making the final theory more compact.