Horn Query Learning with Multiple Refinement
- Sierra, J. 1
- Santibáñez, J. 2
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1
Universitat Politècnica de Catalunya
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2
Universidad de La Rioja
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Editorial: Springer
ISBN: 978-3-540-89694-4
Año de publicación: 2008
Volumen: 5361 LNAI
Páginas: 503-513
Tipo: Capítulo de Libro
Resumen
In this paper we try to understand the heuristics that underlie the decisions made by the Horn query learning algorithm proposed in [1]. We take advantage of our explicit representation of such heuristics in order to present an alternative termination proof for the algorithm, as well as to justify its decisions by showing that they always guarantee that the negative examples in the sequence maintained by the algorithm violate different clauses in the target formula. Finally, we propose a new algorithm that allows multiple refinement when we can prove that such a refinement does not affect the independence of the negative examples in the sequence maintained by the algorithm. © 2008 Springer Berlin Heidelberg.