MC-TopLog: complete multi-clause learning guided by a top theory

Authors:
Stephen H. Muggleton;Dianhuan Lin;Alireza Tamaddoni-Nezhad
Affiliations:
Department of Computing, Imperial College London, UK;Department of Computing, Imperial College London, UK;Department of Computing, Imperial College London, UK
Venue:
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Year:
2011

Citing 12
Cited 0

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ProGolem: a system based on relative minimal generalisation

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Does multi-clause learning help in real-world applications?

ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming

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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.