MC-TopLog: complete multi-clause learning guided by a top theory
ILP(2011)
摘要
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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关键词
incomplete method,complete method,complete multi-clause,game strategy,real-world application,top theory,higher predictive accuracy,ilp system,incomplete ilp system,hypothesis finding
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