| 1996 Publications |
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| Knowledge Based Systems that Have Some Idea of Their Limits |
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| P. Compton, P. Preston, G. Edwards and B. Kang
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| Tenth Knowledge Acquisition for Knowledge-Based Systems Workshop |
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| Validating Incremental Knowledge Acquisition for Multiple Classifications |
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| B. Kang, P. Compton and P. Preston
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| The Third World Congress on Expert Systems |
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| Ripple Down Rules (RDR) is an approach to building knowledge based systems (KBS) which allow an expert to build and maintain a KBS without the assistance of a knowledge engineer or knowledge engineering expertise. RDR allows for a KBS to be built whilst the system is in use, by correcting errors as they occur. Existing RDR inference methods provide a single classification for a set of data. This paper describes an extension to RDR, Multiple Classification Ripple Down Rules (MCRDR), which allows multiple independent classifications but has the same ease of use of RDR. The approach has been evaluated by a series of simulation studies in which the role of human expert is replaced by a simulated expert using another KBS. These studies indicate MCRDR perform at least as well as RDR even in a single classification domain. It seems likely that as well as dealing with multiple classifications, MCRDR will provide a basis for building systems for other problems beyond simple classification but without requiring knowledge engineering assistance. |
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| Verification and Validation with Ripple Down Rules |
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| B. Kang, W. Gambetta and P. Compton
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| International Journal of Human-Computer Studies |
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| Verification to ensure a system's consistency and validation to meet the user's criteria are essential elements in developing knowledge-based systems for real world use. The normal practice is that there will be initial knowledge acquisition attempting to build a complete system which will (should) then be verified and validated. There may be a cycle through these steps till the system is complete. Maintenance is seen as a minor problem requiring the occasional repetition of the three stage process. The implicit assumption is that an expert has complete knowledge and that by a suitable knowledge acquisition process this is acquired. In fact, it seems rather that experts are incapable of recounting how they reach a conclusion. Rather, when asked a question they justify that their conclusion is correct and their justification is tailored to the specific context of the inquiry. Experts are best at justifying why one conclusion is to be preferred over another. This leads to a knowledge acquisition methodology, Ripple-down Rules, in which the knowledge base undergoes on-going development based on correcting errors. Each new correction or justification is considered only in the context of the same mistake being made. The method also constrains the expert's choices to ensure that any new knowledge added is valid while the knowledge base structure ensures the knowledge is verified. Verification and validation are not separate tasks, but constraints on knowledge acquisition which itself continues throughout the life of the system. This provides a closer match with the normal evolution of human knowledge and expertise. The overall approach has itself been validated by the development of a large medical expert system and through simulation studies. The medical system has been developed while in routine use and has only involved experts without any knowledge engineering support or skill in its development. |
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| Help Desk Systems with Intelligence Interface |
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| B. Kang, K. Yohida, H. Motoda, P. Compton and M. Iwayama
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| The Pacific Knowledge Acquisition Workshop |
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| An Implementation of Multiple Classification Ripple Down Rules |
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| P. Preston, P. Compton, G. Edwards and B. Kang
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| Tenth Knowledge Acquisition for Knowledge-Based Systems Workshop |
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