Topics in Expert System Design: Methodologies and ToolsGiovanni Guida, Carlo Tasso North-Holland, 1989 - 441 pages Expert Systems are so far the most promising achievement of artificial intelligence research. Decision making, planning, design, control, supervision and diagnosis are areas where they are showing great potential. However, the establishment of expert system technology and its actual industrial impact are still limited by the lack of a sound, general and reliable design and construction methodology. |
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Page 385
... reliability ; the second is its validity ( or more precisely , the empirical validity ) ; and the third is its usability . Aspects such as verification , in the software engineering sense , are not considered as a part of the evaluation ...
... reliability ; the second is its validity ( or more precisely , the empirical validity ) ; and the third is its usability . Aspects such as verification , in the software engineering sense , are not considered as a part of the evaluation ...
Page 386
... Reliability In addition to the reliability of the evaluation method one may also consider the reliability of the expert system as such . Reliability in technological or ' mechanical ' systems is normally jeopardized by the breakdown of ...
... Reliability In addition to the reliability of the evaluation method one may also consider the reliability of the expert system as such . Reliability in technological or ' mechanical ' systems is normally jeopardized by the breakdown of ...
Page 406
... reliability of the evaluation method . In the first case it is done simply by reducing the variability among the judging experts , by restricting the sample and increasing the level of skill and experience . In the second case it is ...
... reliability of the evaluation method . In the first case it is done simply by reducing the variability among the judging experts , by restricting the sample and increasing the level of skill and experience . In the second case it is ...
Contents
From life cycle to development | 3 |
Choosing an expert system domain | 27 |
Tools and motivations | 47 |
Copyright | |
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abstract activities AI Magazine application approach Artificial Intelligence assessment attribute backward chaining behavior Breuker building cognitive complete components Computer concepts conceptual model construction context cycle decision defined described diagnosis domain expert domain knowledge environment example expert system development expert system evaluation expert system technology expertise facilities Figure formal function goal graphical heuristics identified implementation important inductive input instance integrated interaction interface interpretation models KADS KCML knowledge acquisition knowledge base Knowledge Craft knowledge elicitation knowledge engineer knowledge representation knowledge-based systems KRITON language layer LISP machine machine learning metaclasses methodology methods model-based reasoning MYCIN objects operations OPS5 output particular performance phase possible problem solving problem solving process produce programming Prolog protocol analysis prototype refinement relations reliability repertory grid represent requirements rule-based rules selection shells situations software engineering solution specific strategies target system task techniques types validity values