C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
On a Sufficient Condition for the Existence of Most Specific Hypothesis in Progol
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Which Hypotheses Can Be Found with Inverse Entailment?
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Undecidability of the Horn-clause implication problem
SFCS '92 Proceedings of the 33rd Annual Symposium on Foundations of Computer Science
Theory Completion Using Inverse Entailment
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Induction, Abduction, and Consequence-Finding
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
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In this paper, we introduce a complete algorithm for computing the most specific hypothesis (MSH) in Inverse Entailment when the background knowledge is a set of definite clauses and the positive example is a ground atom having the same predicate symbol as that of the target predicate to be learned. Muggleton showed that for any first order theory (background knowledge) B and a single clause (a positive example) E, the MSH can be computed by first computing all ground (positive and negative) literals which logically follow from B∧¬E and negating their conjunction. However, Yamamoto gave a counter example and indicated that Muggleton's proof contains error. Furukawa gave a sufficient condition to guarantee the above algorithm to compute the MSH. Yamamoto defined a class of problems where the algorithm computes the MSH. In this paper, we extend the MSH computation algorithm to ensure that it computes the MSH correctly under the condition described above.