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Two decades of Ripple Down Rules research

  • Debbie Richards (a1)

Ripple Down Rules (RDR) were developed in answer to the problem of maintaining medium to large rule-based knowledge systems. Traditional approaches to knowledge-based systems gave little thought to maintenance as it was expected that extensive upfront domain analysis involving a highly trained specialist, the knowledge engineer, and the time-poor domain expert would produce a complete model capturing what was in the expert’s head. The ever-changing, contextual and embrained nature of knowledge were not a part of the philosophy upon which they were based. RDR was a paradigm shift, which made knowledge acquisition and maintenance one and the same thing by incrementally acquiring knowledge as domain experts directly interacted with naturally occurring cases in their domain. Cases played an integral part of the acquisition process by motivating the capture of new knowledge, framing the context in which new knowledge would apply and ensuring that previously correctly classified cases remained so by requiring that the classification of the new case distinguish it from the system’s classification and be justified by features of the new case. RDR has moved beyond its first representation which handled single classification tasks within the domain of pathology to support multiple conclusions across a wide range of domains such as help-desk support, email classification and RoboCup and problem types including configuration, simulation, planning and natural language processing. This paper reviews the history of RDR research over the past two decades with a view to its future.

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G. Beydoun , A. Hoffmann 1997. Acquisition of search knowledge. In Knowledge Acquisition, Modeling and Management, Plaza, E. & Benjamins, R. (eds), 10th European Workshop, EKAW’97, Lecture Notes in Artificial Intelligence 1319, 116. Springer-Verlag.

G. Beydoun , A. Hoffmann 2000. Incremental acquisition of search knowledge. Journal of Human-Computer Studies 52, 493530.

B. Buchanan 1986. Expert systems: working systems and the research literature. Expert Systems 3(1), 3251.

B. Chandrasekaran 1986. Generic tasks in knowledge-based reasoning: high level building blocks for expert system design. IEEE Expert 1(3), 2330.

P. J. Compton , R. Jansen 1990. A philosophical basis for knowledge acquisition. Knowledge Acquisition 2, 241257.

P. Compton , L. Peters , G. Edwards , T. G. Lavers 2006. Experience with ripple-down rules. Knowledge-Based System Journal 19(5), 356362.

G. Edwards , P. Compton 1993. PEIRS: a pathologist maintained expert system for the interpretation of chemical pathology reports. Pathology 25, 2734.

B. R. Gaines , P. Compton 1995. Induction of ripple-down rules applied to modeling large databases. Journal for Intelligent Information Systems 5(3), 211228.

A. G. Hoffmann , A. S Khan 2006. A new approach for the incremental development of retrieval functions for CBR. Applied Artificial Intelligence 20(6), 507542.

B. Kang , K. Yoshida , H. Motoda , P. Compton 1997. A help desk system with intelligence interface. Applied Artificial Intelligence 11, 611631.

M. Kim , P. Compton 2004. Evolutionary document management and retrieval for specialized domains on the web. International Journal of Human Computer Studies 60(2), 201241.

R. Martinez-Bejar , R. Benjamins , F. Martin-Rubio 1997. Designing operators for constructing domain knowledge ontologies. In Knowledge Acquisition, Modeling and Management, Plaza, E. & Benjamins, R. (eds). Lecture Notes in Artificial Intelligence 1319, 159173. Springer-Verlag.

M. Mulholland , P. Preston , P. Haddad , B. Hibbert , P. Compton 1996. Teaching a computer ion chromatography from a database of published methods. Journal of Chromatography 739, 1524.

A. Newell 1982. The knowledge level. Artificial Intelligence 18, 87127.

S. B. Pham , A. G. Hoffmann 2004b. KAFTIE: a new KA framework for building sophisticated information extraction systems. In Engineering Knowledge in the Age of the Semantic Web: 14th International Conference, EKAW’04. Springer-Verlag.

Z. Ramadan , M. Mulholland , D. B. Hibbert , P. Preston , P. Compton , P. R. Haddad 1998. Towards an expert system in ion-exclusion chromatography by means of multiple classification ripple-down rules. Journal of Chromatography A 804(1), 2935.

D. Richards 2000. The reuse of knowledge: a user-centred approach. International Journal of Human Computer Studies 52(3), 553579.

D. Richards 2004. Addressing the ontology acquisition bottleneck through reverse ontological engineering. Journal of Knowledge and Information Systems 6, 402427.

D. Richards , P. Compton 1999. Revisiting Sisyphus I—an incremental approach to resource allocations using ripple-down rules. In 12th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Gaines, B., Kremer, R. & Musen, M. (eds). SRDG Publications, University of Calgary, 7-7.1–7-7.20.

M. Vazey 2006. Stochastic foundations for the case-driven acquisition of classification rules. In 15th International Conference on Knowledge Engineering and Knowledge Management Managing Knowledge in a World of Networks (EKAW 2006), 2–6 October, Podebrady, Czech Republic.

M. Vazey , D. Richards 2006a. A case-classification-conclusion IR-RDR approach to knowledge acquisition: applying a classification logic Wiki to the problem solving process. International Journal of Knowledge Management 2(1), 7288.

S. A. Vere 1980. Multilevel counterfactuals for generalizations of relational concepts and productions. Artificial Intelligence 14, 139164.

R. Wille 1992. Concept lattices and conceptual knowledge systems. Computers and Mathematics Applications 23(6–9), 493515.

Y. Yao , F.-Y. Wang , J. Wang , D. Zeng 2005. Rule + Exception strategies for security information analysis. IEEE Intelligent Systems 20(3), 5257.

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