Linear resolution for consequence finding
Artificial Intelligence
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Hypothesis finding based on upward refinement of residue hypotheses
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Induction as Consequence Finding
Machine Learning
Computational aspects of monotone dualization: A brief survey
Discrete Applied Mathematics
Induction on Failure: Learning Connected Horn Theories
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Towards a logical reconstruction of CF-induction
JSAI'07 Proceedings of the 2007 conference on New frontiers in artificial intelligence
Mode-directed inverse entailment for full clausal theories
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Inverse subsumption for complete explanatory induction
Machine Learning
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CF-induction is a sound and complete procedure for finding hypotheses in full clausal theories. It is based on the principle of Inverse Entailment (IE), and consists of two procedures: construction of a bridge theory and generalization of it. There are two possible ways to realize the generalization task in CF-induction. One uses a single deductive operator, called γ-operator, and the other uses a recently proposed form of inverse subsumption. Whereas both are known to retain the completeness of CF-induction, their logical relationship and empirical features have not been clarified yet. In this paper, we show their equivalence property and clarify the difference on their search strategies, which often leads to significant features on their obtained hypotheses.