Abstract
Ripple-Down Rules (RDR) has been successfully used to implement incremental knowledge acquisition systems. Its success largely depends on the organisation of rules, and less attention has been paid to its knowledge representation scheme. Most RDR used standard production rules and exception rules. With sequential processing, RDR acquires exception rules for a particular rule only after the rule wrongly classifies cases. We propose censored production rules (CPR), to be used for acquiring exceptions when a new rule is created using censor conditions. This approach is useful when we have a large number of validation cases at hand. We discuss inference and knowledge acquisition algorithms and related issues. The approach can be combined with machine learning techniques to acquire censor conditions.
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References
Richards, D.: Two decades of ripple down rules research. The Knowledge Engineering Review 24(2), 159–184 (2009)
Compton, P., Jansen, R.: A philosophical basis for knowledge acquisition. Knowledge Acquisition 2(3), 241–258 (1990)
Kang, B., Compton, P., Preston, P.: Multiple Classification Ripple Down Rules: Evaluation and Possibilities. In: 9th AAAI Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, University of Calgary, Banff (1995)
Mulholland, M., Preston, P., Sammut, C., Hibbert, B., Compton, P.: An expert system for ion chromatography developed using machine learning and knowledge in context. In: Proceedings of the 6th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pp. 258–267. Gordon & Breach Science Publishers, Edinburgh (1993)
Forgy, C., McDermott, J.P.: OPS, A Domain-Independent Production System Language. In: 5th International Joint Conference on Artificial Intelligence, pp. 933–939. William Kaufmann, Cambridge (1977)
Forgy, C.L.: Rete: A fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligence 19(1), 17–37 (1982)
Bench-Capon, T.J.M.: Knowledge Representation - An Approach to Artificial Intelligence. The APIC Series, vol. 32. Academic Press (1990)
Pedersen, K.: Well-structured knowledge bases. AI Expert 4(4), 44–55 (1989)
Melle, W.v.: A domain-independent production-rule system for consultation programs. In: Proceedings of the 6th International Joint Conference on Artificial Intelligence, vol. 2, pp. 923–925. Morgan Kaufmann Publishers Inc., Tokyo (1979)
McDermott, J.: R1: the formative years. Readings from the AI Magazine, 93–101 (1988)
Michalski, R.S., Winston, P.H.: Variable precision logic. Artificial Intelligence 29(2), 121–146 (1986)
Haddawy, P.: Implementation of and Experiments with a Variable Precision Logic Inference System. In: AAAI 1986, pp. 238–242 (1986)
Prati, R.C., Monard, M.C., de Carvalho, A.C.P.L.F.: A Method for Refining Knowledge Rules Using Exceptions. In: ASAI 2003 Simposio Argentino de Inteligencia Artificial, Buenos Aires, Argentina (2003)
Cao, T.M., Compton, P.: A simulation framework for knowledge acquisition evaluation. In: Proceedings of the Twenty-Eighth Australasian Conference on Computer Science, vol. 38, pp. 353–360. Australian Computer Society, Inc., Newcastle (2005)
Ignizio, J.P.: Introduction to expert systems: the development and implementation of rule-based expert systems (1991)
Liu, B., Hu, M., Hsu, W.: Intuitive representation of decision trees using general rules and exceptions. In: 17th National Conference on Artificial Intelligence, pp. 615–620 (2000)
Jain, N.K., Bharadwaj, K.K.: Some learning techniques in hierarchical censored production rules (HCPRs) system. International Journal of Intelligent Systems 13(4), 319–344 (1998)
Jain, S., Jain, N.K.: A generalized knowledge representation system for context sensitive reasoning: Generalized HCPRs System. Artificial Intelligence Review 30(1-4), 39–52 (2008)
Bharadwaj, K.K., Jain, N.K.: Hierarchical Censored Production Rules (HCPRs) system. Data & Knowledge Engineering 8(1), 19–34 (1992)
Navarro, D.J.: Analyzing the RULEX model of category learning. Journal of Mathematical Psychology 49(4), 259–275 (2005)
Nosofsky, R.M., Palmeri, T.J., McKiley, S.C.: Rule-plus-exception model of classification learning. Psychological Review 101, 53–79 (1994)
Yiyu, Y., Fei-Yue, W., Zeng, D., Jue, W.: Rule+exception strategies for security information analysis. IEEE Intelligent Systems 20(5), 52–57 (2005)
Delgado, M., Ruiz, M.D., Sánchez, D.: Mining Exception Rules. In: Bouchon-Meunier, B., Magdalena, L., Ojeda-Aciego, M., Verdegay, J.-L., Yager, R.R. (eds.) Foundations of Reasoning under Uncertainty. STUDFUZZ, vol. 249, pp. 43–63. Springer, Heidelberg (2010)
Liu, B., Hu, M., Hsu, W.: Multi-level organization and summarization of the discovered rules. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 208–217. ACM, Boston (2000)
Dejean, H.: Learning rules and their exceptions. The Journal of Machine Learning Research 2, 669–693 (2002)
Boicu, C., Tecuci, G., Boicu, M., Marcu, D.: Improving the Representation Space through Exception-Based Learning. In: Sixteenth International Flairs Conference, pp. 336–340. AAAI Press (2003)
Gaines, B.R., Compton, P.: Induction of ripple-down rules applied to modeling large databases. Journal of Intelligent Information Systems 5(3), 211–228 (1995)
Wada, T., Horiuchi, T., Motoda, H., Washio, T.: Characterization of Default Knowledge in Ripple Down Rules Method. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 284–295. Springer, Heidelberg (1999)
Wada, T., Horiuchi, T., Motota, H., Washio, T.: Integrating Inductive Learning and Knowledge Acquisition in the Ripple Down Rules Method. In: 6th Pacific Knowledge Acquisition Workshop, Sydney, Australia, pp. 325–340 (2000)
Compton, P.: Simulating Expertise. In: PKAW 2000: The 2000 Pacific Rim Knowledge Acquisition Workshop, Sydney, Australia (2000)
Li, C., Zhang, Y., Li, X.: OcVFDT: one-class very fast decision tree for one-class classification of data streams. In: Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data, pp. 79–86. ACM, Paris (2009)
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Kim, Y.S., Compton, P., Kang, B.H. (2012). Ripple-Down Rules with Censored Production Rules. In: Richards, D., Kang, B.H. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2012. Lecture Notes in Computer Science(), vol 7457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32541-0_15
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DOI: https://doi.org/10.1007/978-3-642-32541-0_15
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