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Fuzzy-Biased Theory Revision

Knowledge-based systems suffer from three types of theory flaws: incorrectness, inconsistency, and incompleteness [24]. Research in theory revision focuses on the synergistic effects of combining analytical and empirical machine learning (ML) techniques necessary to address such flaws that plague the use of knowledge-based approaches. Theory revision is premised on the idea that revision of an approximate theory is more effective than generating theories entirely from scratch. The past decade has seen a flurry of research devoted to automating theory revision. The vast majority of this research has focused on representational schema including Horn-clauses and production rules [1]. Other work has opted for hybrid approaches that refine knowledge by leveraging the various types of Machine Learning (ML) capabilities such as neural networks.