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 37
... parties who will challenge the system if its results do not favor them politically ( for ex- ample , on appropriation of funds ) , then these challenges may make it much harder to gain system acceptance . 3.7 The Task The task is such ...
... parties who will challenge the system if its results do not favor them politically ( for ex- ample , on appropriation of funds ) , then these challenges may make it much harder to gain system acceptance . 3.7 The Task The task is such ...
Page 224
... parties who are not prejudiced as to the outcome of the selection process . Once weights and algorithms have been chosen , they should be considered inviolable unless they are found to be untenable , in which case they should be ...
... parties who are not prejudiced as to the outcome of the selection process . Once weights and algorithms have been chosen , they should be considered inviolable unless they are found to be untenable , in which case they should be ...
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