Abstract
Logic programs with annotated disjunctions, or LPADs, are an elegant knowledge representation formalism that can be used to combine first order logical and probabilistic inference. While LPADs can be written manually, one can also consider the question of how to learn them from data. Methods for learning restricted classes of LPADs have been proposed before, but the problem of learning any kind of LPADs was still open. In this paper, we describe a reduction of non-recursive LPADs with a finite Herbrand universe to Bayesian networks. This reduction makes it possible to learn such LPADs using standard learning techniques for Bayesian networks. Thus the class of learnable LPADs is extended.
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Blockeel, H., Meert, W. (2007). Towards Learning Non-recursive LPADs by Transforming Them into Bayesian Networks. In: Muggleton, S., Otero, R., Tamaddoni-Nezhad, A. (eds) Inductive Logic Programming. ILP 2006. Lecture Notes in Computer Science(), vol 4455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73847-3_16
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DOI: https://doi.org/10.1007/978-3-540-73847-3_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73846-6
Online ISBN: 978-3-540-73847-3
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