| 1995 Publications |
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| The Use of Simulated Experts in Evaluating Knowledge Acquisition |
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| P. Compton, P. Prestion and B. Kang
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| The 9th Knowledge Acquisition for Knowledge Based Systems Workshop. |
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| Evaluation of knowledge acquisition methods remains an important goal; however, evaluation of actual knowledge acquisition is difficult because of the unavailability of experts for adequately controlled studies. This paper proposes the use of simulated experts, i.e., other knowledge based systems as sources of expertise in assessing knowledge acquisition tools. A simulated expert is not as creative or wise as a human expert, but it readily allows for controlled experiments. This method has been used to assess a knowledge acquisition methodology, Ripple Down Rules at various levels of expertise and shows that redundancy is not a major problem with RDR. |
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| Prudent Expert Systems with Credentials: Managing the Expertise of Decision Support Systems |
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| Edwards G, B. Kang, P. Preston and P. Compton
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| International Journal of Bio-Medical Computing |
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| "Black box" expert systems (ES) are mistrusted by clinicians. Errors generated by medical ES are also a significant cause for concern. We report new ES properties - prudence and credentials - that improve error management and underpin a new approach for improving the credibility of ES for clinical users. |
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| Validating Knowledge Acquisition : Multiple Classification Ripple Down Rules |
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| B. Kang
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| Univeristy of New South Wales, Sydney |
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| Multiple Classificaiton Ripple Down Rules : Evaluation and Possibilities |
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| B. Kang, P. Compton and P. Preston
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| The 9th Knowledge Acquisition for Knowledge Based Systems Workshop. |
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| Ripple Down Rules (RDR) is a knowledge acquisition method which can constrain the interactions between the expert and a shell to acqire only correct knowledge. Although, RDR shows its efficience in practice and experiments, RDR is only for the problem of providing a single classification for a set of data. Multiple Classification Ripple Down Rules (MCRDR) is an extention of RDR and allows multiple independent classifications. The approach has been evaluated by simulation studies in which the human expert is replaced by a simulated expert. MCRDR may provide a basis for building a general problem solver for a range of problems beyond classification. |
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