Abstract
Ripple Down Rules (RDR) is a maturing collection of methodologies for the incremental development and maintenance of medium to large rule-based knowledge systems. While earlier knowledge based systems relied on extensive modeling and knowledge engineering, RDR instead takes a simple no-model approach that merges the development and maintenance stages. Over the last twenty years RDR has been significantly expanded and applied in numerous domains. Until now researchers have generally implemented their own version of the methodologies, while commercial implementations are not made available. This has resulted in much duplicated code and the advantages of RDR not being available to a wider audience. The aim of this project is to develop a comprehensive and extensible platform that supports current and future RDR technologies, thereby allowing researchers and developers access to the power and versatility of RDR. This paper is a report on the current status of the project and marks the first release of the software.
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References
Compton, P., Jansen, R.: Knowledge in Context: a strategy for expert system maintenance. Second Australian Joint Artificial Intelligence Conference (AI88) 1, 292–306 (1988)
Richards, D.: Two decades of Ripple Down Rules research. The Knowledge Engineering Review 24, 159–184 (2009)
Compton, P., Edwards, G., Kang, B., Lazarus, L., Malor, R., Menziès, T., Preston, P., Srinivasan, A., Sammut, C.: Ripple Down Rules: Possibilities and Limitations. In: 6th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop (KAW 1991). SRDG Publications, Canada (1991)
Compton, P., Kang, B., Preston, P., Mulholland, M.: Knowledge Acquisition without Analysis. In: Knowledge Acquisition for Knowledge Based Systems. Springer, Berlin (1993)
Menzies, T.: Towards Situated Knowledge Acquisition. International Journal of Human-Computer Studies 49, 867–893 (1998)
Dazeley, R., Kang, B.H.: Epistemological Approach to the Process of Practice. Journal of Minds and Machines, Springer Science+Business Media B.V. 18, 547–567 (2008)
Dazeley, R.: An Expert System Methodology for SMEs and NPOs. In: 11th Annual Australian Conference on Knowledge Management and Intelligent Decision Support - ACKMIDS 2008 (2008)
Kang, B.H., Compton, P.: Multiple Classification Ripple Down Rules. In: Third Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop. Japanese Society for Artificial Intelligence, Hatoyama, Japan (1994)
Kang, B.H.: Validating Knowledge Acquisition: Multiple Classification Ripple Down Rules. University of New South Wales, Sydney (1996)
Beydoun, G., Hoffmann, A.: NRDR for the Acquisition of Search Knowledge. In: Proceedings of Tenth Australian Joint Conference on Artificial Intelligence, Perth, Australia (1997)
Preston, P., Edwards, G., Compton, P., Litkouthi, D.: An Expert System Interpreter for Time Course Data with Refinement in Context. In: AAAI Spring Symposium: Artificial Intelligence in Medicine (1994)
Shiraz, G.M., Summut, C.A.: An incremental Method for Learning to Control Dynamic Systems. In: The Machine Learning Workshop of the IJCAI 1995, Montreal, Canada (1995)
Martinez-Bejar, R., Benjamins, V., Compton, P., Preston, P., Martin-Rubio, F.: A formal framework to build domain knowledge ontologies for ripple-down rules-based systems. In: 11th Banff Knowledge Acquisition for Knowledge Base System Workshop (KAW 1998), Canada, SRDG (1998)
Richards, D.: Ripple Down Rules with Formal Concept Analysis: A Comparison to Personal Construct Psychology. In: 11th Workshop on Knowledge Acquisition, Modeling and Management (KAW 1998), Banff, Canada, SRDG Publications, Department of Computer Science, University of Calgary, Calgary (1998)
Vazey, M., Richards, D.: Achieving rapid knowledge acquisition in a high-volume call centre. In: Kang, B., Hoffmann, A., Yamaguchi, T., Yeap, W. (eds.) Proceedings of the Pacific Knowledge Acquisition Workshop 2004, Auckland, pp. 74–86 (2004)
Dazeley, R., Kang, B.: Rated MCRDR: Finding non-Linear Relationships between Classifications in MCRDR. In: 3rd International Conference on Hybrid Intelligent Systems, pp. 499–508. IOS Press, Melbourne (2003)
Dazeley, R., Kang, B.H.: Generalising Symbolic Knowledge in Online Classification and Prediction. In: Richards, D., Kang, B.-H. (eds.) PKAW 2008. LNCS, vol. 5465, pp. 91–108. Springer, Heidelberg (2009)
Compton, P., Peters, L., Edwards, G., Lavers, T.: Experience with ripple-down rules. In: Proceedings of AI 2005, the Twenty-Fifth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, UK, pp. 109–121 (December 2005)
Park, S.S., Kim, Y.S., Kang, B.: Personalized Web Document Classification using MCRDR. In: Pacific Rim Knowledge Acquisition Workshop (PKAW 2004), Auckland, New Zealand. Springer, Heidelberg (2004)
Ho, V., Wobcke, W., Compton, P.: EMMA: an E-mail Management Assistant. In: Liu, J., Faltings, B., Zhong, N., Lu, R., Nishida, T. (eds.) IEEE/WIC International Conference on Intelligent Agent Technology, pp. 67–74. IEEE, Los Alamitos (2003)
Mak, P., Kang, B., Sammut, C., Kadous, W.: Knowledge acquisition module for conversational agents. In: Kang, B., Hoffmann, A., Yamaguchi, T., Yeap, W. (eds.) Proceedings of the Pacific Knowledge Acquisition Workshop PKAW 2004, Auckland, pp. 54–62 (2004)
Finlayson, A., Compton, P.: Incremental knowledge acquisition using RDR for soccer simulation. In: Kang, B., Hoffmann, A., Yamaguchi, T., Yeap, W. (eds.) Proceedings of the Pacific Knowledge Acquisition Workshop, PKAW 2004, Auckland, pp. 102–116 (2004)
Gaines, B.R., Compton, P.J.: Induction of Ripple Down Rules. In: Fifth Australian Conference on Artificial Intelligence (AI92). World Scientific, Hobart (1992)
Scheffer, T.: Algebraic Foundation and Improved Methods of Induction of Ripple Down Rules. In: Proceedings of the Pacific Knowledge Acquisition Workshop, PKAW 1996 (1996)
Edwards, G., Compton, P., Malor, R., Srinivasan, A., Lazarus, L.: Peirs: A pathologist-maintained expert system for the interpretation of chemical pathology reports. Pathology 25(1), 27–34 (1993)
Garsden, H., Basilakis, J., Celler, B., Huynh, K., Lovell, N.: A Home Health Monitoring System Including Intelligent Reporting and Alerts. In: EMBC 2004: Annual Conference of the Engineering in Medicine and Biology Society, San Francisco, CA (2004)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)
Compton, P.: Simulating Expertise. In: Proceedings of the 6th Pacific Knowledge Acquisition Workshop, Sydney, Australia (2000)
Dazeley, R., Kang, B.: Detecting the Knowledge Boundary with Prudence Analysis. In: Wobcke, W., Zhang, M. (eds.) AI 2008. LNCS (LNAI), vol. 5360, pp. 482–488. Springer, Heidelberg (2008)
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Dazeley, R., Warner, P., Johnson, S., Vamplew, P. (2010). The Ballarat Incremental Knowledge Engine. In: Kang, BH., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2010. Lecture Notes in Computer Science(), vol 6232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15037-1_17
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