Baffes, P. T., & Mooney, R. J. (1993). Symbolic revision of theories with M-of-N rules. Proceedings of the Thirteenth International Joint Conference in Artificial Intelligence (pp. 1135–1140). Chambery: France.
Bairess, R., Porter, B. W., & Murray, K. S. (1989). Supporting start-to-finish development of knowledge bases. Machine Learning
, 259–283.Google Scholar
Boose, J. H., & Gaines, B. R. (1989). Knowledge acquisition for knowledge-based systems: Notes on the stateof-the-art. Machine Learning
, 377–394.Google Scholar
Boswell, R., Craw, S., & Rowe, R. (1997). Knowledge refinement for a design system. Proceedings of the European Knowledge Acquisition Workshop (pp. 49–64). Springer.
Brunk, C., & Pazzani, M. (1995). A Linguistically-based semantic bias for theory revision. Proceedings of the Twelth International Conference on Machine Learning. Lake Tahoe: California.
Carbonara, L. (1996). Improving the effectiveness and the efficiency of knowledge base refinement. PhD Thesis, University of Aberdeen, Aberdeen: Scotland.Google Scholar
Carbonara, L., & Sleeman, D. H. (1996). Improving the efficiency of knowledge base refinement. Proceedings of the Thirteenth International Conference on Machine Learning (pp. 78–86). Bari: Italy.
Craw, S., & Hutton, P. (1995). Protein folding: Symbolic refinement competes with neural networks. Proceedings of the Twelfth International Conference on Machine Learning (pp. 133–141). Lake Tahoe: California.
Craw, S., & Sleeman, D. H. (1990). Automating the refinement of knowledge-based systems. In L. C. Aiello (Ed.), Proceedings of the Ninth European Conference on Artificial Intelligence (pp. 167–172). Stockholm: Sweden.
Craw, S., Sleeman, D. H., Boswell, R., & Carbonara, L. (1994). Is knowledge refinement different from theory revision? In S. Wrobel (Ed.), Proceedings of the MLNet Familiarization Workshop on Theory Revision and Restructuring in Machine Learning (at ECML-94, Catania, Italy). Arbeitspapiere der GMD, GMD, Pf. 1316, 53754 Sankt Augustin: Germany.
de Kleer, J. (1986). An assumption-based TMS. Artificial Intelligence
, 197–244.Google Scholar
Donoho, S. K., & Rendell, L. A. (1995). Rerepresenting and restructuring domain theories: A constructive induction approach. Journal of Artificial Intelligence Research
, 411–446.Google Scholar
Doyle, J. (1979). A truth maintenance system. Artificial Intelligence
, 231–272.Google Scholar
Emde, W. (1989). An inference engine for representing multiple theories. In K. Morik (Ed.), Knowledge reporesentation and organization in machine learning. Springer Verlag.
Feigenbaum, E. A. (1977). The art of artificial intelligence: Themes and case studies in knowledge engineering. Proceedings of IJCAI-77 (pp. 1014–1029).
Forbus, K. D., & de Kleer, J. (1993). Building problem solvers
. Cambridge, Massachusset: MIT Press.Google Scholar
Ginsberg, A. (1988a). Automatic refinement of expert system knowledge bases
. San Mateo, California: Morgan Kaufmann.Google Scholar
Ginsberg, A. (1988b). Theory revision via prior operationalization. Proceedings of the Seventh National Conference on Artificial Intelligence
(Vol. 2, pp. 590–595). Minneapolis, MN: Morgan Kaufmann.Google Scholar
Hekanaho, J. (1996). Background knowledge in GA-based concept learning. Proceedings of the Thirteenth International Conference on Machine Learning (pp. 234–242). Bari: Italy.
Koppel, M., Ronen, F., & Segre, A. M. (1994). Bias-driven revision of logical domain theories. Journal of Artificial Intelligence Research
, 159–208.Google Scholar
Mahoney, J. J., & Mooney, R. J. (1993). Combining connectionist and symbolic learning to refine certainty-factor rule-bases. Connection Science
, 339–364.Google Scholar
McAllester, D. (1982). Reasoning utility package user's manual
. (AI Lab Report 667) Cambridge, MA: MIT.Google Scholar
Michalski, R. S., & Chilausky, S. (1980). Learning by being told and learning from examples: An experimental comparison of the two methods of knowledge acquisition in the context of developing an expert system for soybean disease diagnosis. Journal of Policy Analysis and Information Systems
(2), 126–161.Google Scholar
Opitz, D.W., & Shavlik, J.W. (1994). Using genetic search to refine knowledge-based neural networks. Proceedings of the Eleventh International Conference on Machine Learning. San Francisco, CA.
Ourston, D., & Mooney, R. (1990). Changing the rules: A comprehensive approach to theory refinement. Proceedings of the Eighth National Conference on Artificial Intelligence (pp. 815–820). Menlo Park, CA.
Politakis, P., & Weiss, S. (1984). Using empirical analysis to refine expert system knowledge bases. Artificial Intelligence
, 23–48.Google Scholar
Popchev, I., Zlatareva, N., & Mircheva M. (1990). A truth maintenance theory: An alternative approach. Proceedings of the Ninth European Conference on Artificial Intelligence
(pp. 509–514). Stockholm, Sweden: Pitman Publ.Google Scholar
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning
, 81–106.Google Scholar
Richards, B. L., & Mooney, R. J. (1995). Automated refinement of first-order Horn-clause domain theories. Machine Learning
, 95–131.Google Scholar
Tangkitvanich, S., & Shimura, M. (1992). Refining a relational theory with multiple faults in the concept and subconcepts. Proceedings of the Ninth International Conference on Machine Learning
(pp. 436–444). Aberdeen, UK: Morgan Kaufmann.Google Scholar
Towell, G. G., & Shavlik, J. W. (1992). Interpretation of artificial neural networks: Mapping knowledge-based neural networks into rules. In R. Lippmann, J. Moody, & D. Touretzky (Eds.), Advances in neural information processing systems (Vol. 4). Morgan Kaufmann.
Towell, G. G., Shavlik, J. W., & Noordewier, M. O. (1990). Refinement of approximate domain theories by knowledge-based neural networks. Proceedings of the Eight National Conference on Artificial Intelligence. Boston, Massachusetts.
Wilkins, D. C. (1990). Knowledge base refinement as improving an incorrect and incomplete domain theory. In Y. Kodratoff & R. Michalski (Eds.), Machine learning, an AI approach (Vol. III). Morgan Kauffmann.
Winston, P. H., Binford, T. O., Katz, & Lowry, M. (1983). Learning physical descriptions from functional definitions, examples, and precedents. Proceedings of the National Conference on Artificial Intelligence (pp. 433–439). Washington, D.C.
Wogulis, J., & Pazzani, M. (1993). A methodology for evaluating theory revision systems. Results with AUDREY II. Proceedings of the International Joint Conference on Artificial Intelligence. Chambery, France.
Wrobel, S. (1994). Concept formation during interactive theory revision. Machine Learning
, 169–191.Google Scholar
Zlatareva, N. P. (1994). A framework for verification, validation, and refinement of knowledge bases: The VVR system. International Journal of Intelligent Systems
, 703–737.Google Scholar