Artificial Intelligence
Strategies in Combined Learning via Logic Programs
Machine Learning - Special issue on multistrategy learning
Alternative foundations for Reiter's default logic
Artificial Intelligence
Learning Logical Definitions from Relations
Machine Learning
Adding Priorities and Specificity to Default Logic
JELIA '94 Proceedings of the European Workshop on Logics in Artificial Intelligence
Induction of Constraint Logic Programs
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Learning Non-Monotonic Logic Programs: Learning Exceptions
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Integrating explanatory and descriptive learning in ILP
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
Learning extended logic programs
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
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We present a method to learn simultaneously definitions for a concept and its negation. This problem is relevant when we have to deal with a complex domain where it is difficult to acquire a complete theory and where we have to reason from incomplete knowledge. We use default logic to represent such incomplete theories. This paper specifies the problem of learning a default theory from a set of examples and a background knowledge.We propose an operational method to inductively construct such a theory. Our learning process relies on a generalization mechanism defined in the field of Inductive Logic Programming. We first consider the case where the initial knowledge is sure because it contains only ground facts. Then, we extend the framework to the case where the initial knowledge is a default theory.