Abstract
Several learning systems based on Inverse Entailment (IE) have been proposed, some that compute single clause hypotheses, exemplified by Progol, and others that produce multiple clauses in response to a single seed example. A common denominator of these systems is a restricted hypothesis search space, within which each clause must individually explain some example E, or some member of an abductive explanation for E. This paper proposes a new IE approach, called Induction on Failure (IoF), that generalises existing Horn clause learning systems by allowing the computation of hypotheses within a larger search space, namely that of Connected Theories. A proof procedure for IoF is proposed that generalises existing IE systems and also resolves Yamamoto’s example. A prototype implementation is also described. Finally, a semantics is presented, called Connected Theory Generalisation, which is proved to extend Kernel Set Subsumption and to include hypotheses constructed within this new IoF approach.
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Kimber, T., Broda, K., Russo, A. (2009). Induction on Failure: Learning Connected Horn Theories. In: Erdem, E., Lin, F., Schaub, T. (eds) Logic Programming and Nonmonotonic Reasoning. LPNMR 2009. Lecture Notes in Computer Science(), vol 5753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04238-6_16
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DOI: https://doi.org/10.1007/978-3-642-04238-6_16
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