PFORTE: Revising Probabilistic FOL Theories

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Abstract

There has been significant recent progress in the integration of probabilistic reasoning with first order logic representations (SRL). So far, the learning algorithms developed for these models all learn from scratch, assuming an invariant background knowledge. As an alternative, theory revision techniques have been shown to perform well on a variety of machine learning problems. These techniques start from an approximate initial theory and apply modifications in places that performed badly in classification. In this work we describe the first revision system for SRL classification, PFORTE, which addresses two problems: all examples must be classified, and they must be classified well. PFORTE uses a two step-approach. The completeness component uses generalization operators to address failed proofs and the classification component addresses classification problems using generalization and specialization operators. Experimental results show significant benefits from using theory revision techniques compared to learning from scratch.