Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Intensional updates: abduction via deduction
Logic programming
Handbook of theoretical computer science (vol. B)
Belief updating from integrity constraints and queries
Artificial Intelligence
The acceptability semantics for logic programs
Proceedings of the eleventh international conference on Logic programming
A unifying view for logic programming with non-monotonic reasoning
Theoretical Computer Science
Algorithmic Program DeBugging
Inductive Logic Programming: From Machine Learning to Software Engineering
Inductive Logic Programming: From Machine Learning to Software Engineering
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
RUTH: an ILP Theory Revision System
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Introducing Abduction into (Extensional) Inductive Logic Programming Systems
AI*IA '97 Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Learning Non-Monotonic Logic Programs: Learning Exceptions
ECML '95 Proceedings of the 8th European Conference on Machine Learning
AILP abductive inductive logic programming
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Embracing causality in formal reasoning
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
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We propose an approach for the integration of abduction and induction in Logic Programming. We define an Abductive Learning Problem as an extended Inductive Logic Programming problem where both the background and target theories are abductive theories and where abductive derivability is used as the coverage relation instead of deductive derivability. The two main benefits of this integration are the possibility of learning in presence of incomplete knowledge and the increased expressive power of the background and target theories. We present the system LAP (Learning Abductive Programs) that is able to solve this extended learning problem and we describe, by means of examples, four different learning tasks that can be performed by the system: learning from incomplete knowledge, learning rules with exceptions, learning from integrity constraints and learning recursive predicates.