A philosophical basis for knowledge acquisition
Knowledge Acquisition
Artificial Intelligence
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Second generation expert systems
Case-based reasoning
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IEEE Software
The Improvement of Response Modeling: Combining Rule-Induction and Case-Based Reasoning
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Validating knowledge acquisition: multiple classification ripple-down rules
Validating knowledge acquisition: multiple classification ripple-down rules
Comparing the conceptual systems of experts
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Worklets: a service-oriented implementation of dynamic flexibility in workflows
ODBASE'06/OTM'06 Proceedings of the 2006 Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part I
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Providing contextualised knowledge often involves the difficult and time-consuming task of specifying the appropriate contexts in which the knowledge applies. This paper describes the Ripple Down Rules (RDR) knowledge acquisition and representation technique which does not attempt to define up front the possible context/s. Instead cases and the exception structure provide the context and rules provide the index by which to retrieve the case/s. KA is incremental. The domain expert locally patches rules as new cases are seen. Thus, RDR is a hybrid case-based and rule-based approach. The use of Formal Concept Analysis to translate the RDR performance system into a formal context and uncover an explantation system in the from of an abstraction hierarchy further strengthens our emphasis on the combined use of cases and rules to provide contextualised knowledge.