Combining Neural and Symbolic Learning to Revise Probabilistic Rule Bases

Abstract

This paper describes RAPTURE- a system for revising probabilis-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-fining two actual expert knowledge bases demonstrate that this combined approach performs better than previous methods. 1