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 12
... phases of our life cycle are : Phase 1 - Plausibility study Phase 2 - Demonstration prototype construction Phase 3 - - Full prototype construction Phase 4 Target system implementation and installation Phase 5 - Operation , maintenance ...
... phases of our life cycle are : Phase 1 - Plausibility study Phase 2 - Demonstration prototype construction Phase 3 - - Full prototype construction Phase 4 Target system implementation and installation Phase 5 - Operation , maintenance ...
Page 14
... Phase 2 - Demonstration prototype construction The main goal of Phase 2 is to develop and demonstrate a first limited prototype of the complete expert system . The major output of this phase is therefore a running expert system , called ...
... Phase 2 - Demonstration prototype construction The main goal of Phase 2 is to develop and demonstrate a first limited prototype of the complete expert system . The major output of this phase is therefore a running expert system , called ...
Page 291
... phase . An important assumption in KADS is that the Analysis phase and Design phase can be strictly separated . In a number of early studies before the KADS Design phase ( and language ) was developed- , implementation directly follows ...
... phase . An important assumption in KADS is that the Analysis phase and Design phase can be strictly separated . In a number of early studies before the KADS Design phase ( and language ) was developed- , implementation directly follows ...
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