A philosophical basis for knowledge acquisition
Knowledge Acquisition
Neural computing: an introduction
Neural computing: an introduction
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Local Patching Produces Compact Knowledge Bases
EKAW '94 Proceedings of the 8th European Knowledge Acquisition Workshop on A Future for Knowledge Acquisition
The Ballarat incremental knowledge engine
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
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Multiple Classification Ripple Down Rules (MCRDR) is a simple and effective knowledge acquisition technique that produces representations, or knowledge maps, of a human expert's knowledge of a particular domain. This knowledge map can then be used to automate and help the user perform classification and categorisation of cases while still being able to add more refined knowledge incrementally. While MCRDR has been applied in many domains, work on understanding the meta-knowledge acquired or using the knowledge to derive new information is still in its infancy. This paper will introduce a technique called Rated MCRDR (RM), which looks at deriving and learning information about both linear and non-linear relationships between the mnultiple clasifications within MCRDR. This method uses the knowledge received in the MCRDR knowledge map to derive additional information that allows improvements in functionality within existing domains, to which MCRDR is currently applied, as well as opening up the possibility of new problem domains. Preliminary testing shows that there exists a strong potential for RM to quickly and effectively learn meaningful ratings.