C4.5: programs for machine learning
C4.5: programs for machine learning
Towards situated knowledge acquisition
International Journal of Human-Computer Studies
Knowledge Acquisition without Analysis
Proceedings of the 7th European Workshop on Knowledge Acquisition for Knowledge-Based Systems
Knowledge in Context: A Strategy for Expert System Maintenance
AI '88 Proceedings of the 2nd Australian Joint Artificial Intelligence Conference
NRDR for the Acquisition of Search Knowledge
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
EMMA: An E-Mail Management Assistant
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Rated MCRDR: finding non-linear relationships between classifications in MCRDR
Design and application of hybrid intelligent systems
Epistemological Approach to the Process of Practice
Minds and Machines
Detecting the Knowledge Boundary with Prudence Analysis
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Two decades of ripple down rules research
The Knowledge Engineering Review
Generalising Symbolic Knowledge in Online Classification and Prediction
Knowledge Acquisition: Approaches, Algorithms and Applications
Detection of CAN by ensemble classifiers based on ripple down rules
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
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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.