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 90
... early strong influence of the domain of clinical medicine led to experience - based judgemental knowledge as the fundamental knowledge content of expert systems . 82 ] . There is an irony that experential knowledge became the knowledge ...
... early strong influence of the domain of clinical medicine led to experience - based judgemental knowledge as the fundamental knowledge content of expert systems . 82 ] . There is an irony that experential knowledge became the knowledge ...
Page 182
... earliest tools used in expert system building was LISP . LISP is a versatile language and can support a rich variety of representational structures and most of the early expert systems such as MYCIN , PROSPECTOR etc. were built using ...
... earliest tools used in expert system building was LISP . LISP is a versatile language and can support a rich variety of representational structures and most of the early expert systems such as MYCIN , PROSPECTOR etc. were built using ...
Page 291
... early studies before the KADS Design phase ( and language ) was developed- , implementation directly follows the analysis , and the conceptual model is used more or less as an architecture of the system . In most of the recent studies ...
... early studies before the KADS Design phase ( and language ) was developed- , implementation directly follows the analysis , and the conceptual model is used more or less as an architecture of the system . In most of the recent studies ...
Contents
From life cycle to development | 3 |
Choosing an expert system domain | 27 |
Tools and motivations | 47 |
Copyright | |
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abstract activities addition allow analysis application approach attribute building complete components Computer concepts conclusion considered consists construction contains decision defined depends described detailed discussed domain effective elicitation environment evaluation example expert system expertise facilities fact Figure formal frame function given goal human identified implementation important inference input instance integrated Intelligence interaction interface International interpretation knowledge acquisition knowledge base knowledge engineer knowledge representation knowledge-based language learning limited machine major means mechanisms methodology methods objects operations output particular performance phase possible practical presented problem solving produce programming Prolog prototype question reasoning refer refinement relations Report represent representation requirements rules selection shells shows situations solution specific strategies structure studies system development task techniques types validity values