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. |
From inside the book
Results 1-3 of 44
Page 121
... CONCLUSION defines the decision or action which is executed when the CONDI- TION is satisfied by a given situation ; a is the strength of the evidence which supports the CONCLUSION when the CON- DITION is completely satisfied ( 0 ≤ a ...
... CONCLUSION defines the decision or action which is executed when the CONDI- TION is satisfied by a given situation ; a is the strength of the evidence which supports the CONCLUSION when the CON- DITION is completely satisfied ( 0 ≤ a ...
Page 276
... conclusion ( Figure 2a ) . The hypothesis may be a particular fault ; the evidence of identified values for parameters in a working device ; the conclusion consist of a true / false value for the particular fault . Note that we have ...
... conclusion ( Figure 2a ) . The hypothesis may be a particular fault ; the evidence of identified values for parameters in a working device ; the conclusion consist of a true / false value for the particular fault . Note that we have ...
Page 361
... Conclusions In a goal - driven production system , a conclusion of a rule should either match a goal or match an IF condition of another rule . If there are no matches for the conclusion , it is said to be unreachable . For example ...
... Conclusions In a goal - driven production system , a conclusion of a rule should either match a goal or match an IF condition of another rule . If there are no matches for the conclusion , it is said to be unreachable . For example ...
Contents
From life cycle to development | 3 |
Choosing an expert system domain | 27 |
Tools and motivations | 47 |
Copyright | |
14 other sections not shown
Other editions - View all
Common terms and phrases
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