Abstract
Within ILP much effort has been put into designing methods that are complete for hypothesis finding. However, it is not clear whether completeness is important in real-world applications. This paper uses a simplified version of grammar learning to show how a complete method can improve on the learning results of an incomplete method. Seeing the necessity of having a complete method for real-world applications, we introduce a method called ⊤-directed theory co-derivation, which is shown to be correct (ie. sound and complete). The proposed method has been implemented in the ILP system MC-TopLog and tested on grammar learning and the learning of game strategies. Compared to Progol5, an efficient but incomplete ILP system, MC-TopLog has higher predictive accuracies, especially when the background knowledge is severely incomplete.
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Muggleton, S.H., Lin, D., Tamaddoni-Nezhad, A. (2012). MC-TopLog: Complete Multi-clause Learning Guided by a Top Theory. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds) Inductive Logic Programming. ILP 2011. Lecture Notes in Computer Science(), vol 7207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31951-8_22
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