This paper describes RAPTURE - tic knowledge bases that combines neural and symbolic learning methods. RAPTURE uses a modified version of backpropagation to refine the certainty factors of a MYCIN-style rule base and uses ID3's information gain heuristic to add new rules. Results on re(cid:173) fining two actual expert knowledge bases demonstrate that this combined approach performs better than previous methods.

Recently, both connectionist and symbolic methods have been developed for biasing learning with prior knowledge lFu, 1989; Towell et a/., 1990; Ourston and Mooney, 1990]. Most ofthese 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 successfully applied to real-world tasks, such as recognizing promoter sequences in DNA [Towell et ai., 1990; Ourston and Mooney, 1990]. The results demonstrate that revising an expert-given knowledge base produces more accurate results than learning from training data alone.

Recently, both connectionist and symbolic methods have been developed for biasing learning with prior knowledge lFu, 1989; Towell et a/., 1990; Ourston and Mooney, 1990]. Most ofthese 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 successfully applied to real-world tasks, such as recognizing promoter sequences in DNA [Towell et ai., 1990; Ourston and Mooney, 1990]. The results demonstrate that revising an expert-given knowledge base produces more accurate results than learning from training data alone.

Recently, both connectionist and symbolic methods have been developed for biasing learning with prior knowledge lFu,1989; Towell et a/., 1990; Ourston and Mooney, 1990]. Most ofthese 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 successfully applied to real-world tasks, such as recognizing promoter sequences in DNA [Towell et ai., 1990; Ourston and Mooney, 1990]. The results demonstrate that revising an expert-given knowledge base produces more accurate results than learning from training data alone. Inthis paper, we describe the RAPTURE system (Revising Approximate 107 108 Mahoney and Mooney Probabilistic Theories Using Repositories of Examples), which combines connectionist andsymbolic methods to revise both the parameters and structure of a certainty-factor rule base. 2 The Rapture Algorithm