This paper describes RAPTURE --- a system for revising probabilistic rule bases that converts symbolic rules into a connectionist network, which is then trained via connectionist techniques. It uses a modified version of backpropagation to refine the certainty factors of the rule base, and uses ID3's information-gain heuristic (Quinlan) to add new rules. Work is currently under way for finding improved techniques for modifying network architectures that include adding hidden units using the UPSTART algorithm (Frean). A case is made via comparison with fully connected connectionist techniques for keeping the rule base as close to the original as possible, adding new input units only as needed.

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In Proceedings of the International Symposium on Integrating Knowledge and Neural Heuristics (ISIKNH-94), pp. 75--85, Pensacola, FL, May 1994.

@inproceedings{mahoney:isiknh94, title={Modifying Network Architectures For Certainty-Factor Rule-Base Revision}, author={J. Jeffrey Mahoney and Raymond J. Mooney}, booktitle={Proceedings of the International Symposium on Integrating Knowledge and Neural Heuristics (ISIKNH-94)}, month={May}, address={Pensacola, FL}, pages={75--85}, url="http://www.cs.utexas.edu/users/ai-lab?mahoney:isiknh94", year={1994} }