Learning Conjunctions of Horn Clauses
Machine Learning - Computational learning theory
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Selected papers of the 23rd annual ACM symposium on Theory of computing
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COLT '95 Proceedings of the eighth annual conference on Computational learning theory
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Journal of the ACM (JACM)
Artificial Intelligence
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Symbolic Logic and Mechanical Theorem Proving
Theoretical Computer Science - Special issue: Algorithmic learning theory
IBM Journal of Research and Development
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Information and Computation
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We describe an alternative construction of an existing canonical representation for definite Horn theories, the Guigues-Duquenne basis (or GD basis), which minimizes a natural notion of implicational size. We extend the canonical representation to general Horn, by providing a reduction from definite to general Horn CNF. We show how this representation relates to two topics in query learning theory: first, we show that a well-known algorithm by Angluin, Frazier and Pitt that learns Horn CNF always outputs the GD basis independently of the counterexamples it receives; second, we build strong polynomial certificates for Horn CNF directly from the GD basis.