Abstract

This paper describes Rapture --- a system for revising probabilistic knowledge bases that combines connectionist and symbolic learning methods. Rapture uses a modified version of backpropagation to refine the certainty factors of a probabilistic rule base and it uses ID3's information-gain heuristic to add new rules. Results on refining three actual expert knowledge bases demonstrate that this combined approach generally performs better than previous methods. 1 Introduction In complex domains, learning needs to be biased with prior knowledge in order to produce satisfactory results from limited training data. Recently, both connectionist and symbolic methods have been developed for biasing learning with prior knowledge (Shavlik and Towell, 1989; Fu, 1989; Ourston and Mooney, 1990; Pazzani and Kibler, 1992; Cohen, 1992). Most of these methods revise an imperfect knowledge base (usually obtained from a domain expert) to fit a set of empirical data. Some of these methods have been succ...

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