Topics in Expert System Design: Methodologies and ToolsC. Tasso, G. Guida Elsevier, 2014 M06 28 - 447 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.This book has a dual purpose: to offer concrete guidelines and tools to the designers of expert systems, and to promote basic and applied research on methodologies and tools. It is a coordinated collection of papers from researchers in the USA and Europe, examining important and emerging topics, methodological advances and practical experience obtained in specific applications. Each paper includes a survey introduction, and a comprehensive bibliography is provided. |
From inside the book
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Page 6
... formal grammars, it requires a lot of training before it can be applied) and it is characterized by a low level of portability (both the grammar and the tools employed have been developed for the electronic troubleshooting domain 6 G ...
... formal grammars, it requires a lot of training before it can be applied) and it is characterized by a low level of portability (both the grammar and the tools employed have been developed for the electronic troubleshooting domain 6 G ...
Page 15
... formal demonstrations take place so as to allow an extensive evaluation of the obtained results. Phase 3 - Full prototype construction The main goal of Phase 3 is to develop a full expert system, called full prototype (or simply ...
... formal demonstrations take place so as to allow an extensive evaluation of the obtained results. Phase 3 - Full prototype construction The main goal of Phase 3 is to develop a full expert system, called full prototype (or simply ...
Page 16
... formal testing is carried on. To this purpose, it may often be necessary to develop a simulator, which can simulate interaction of the prototype with the real operational environment. The prototype is tested with realistic data samples ...
... formal testing is carried on. To this purpose, it may often be necessary to develop a simulator, which can simulate interaction of the prototype with the real operational environment. The prototype is tested with realistic data samples ...
Page 38
... formal manner before. • Experts would agree on whether the system's results are correct (or, when there is no single correct result, acceptable). If experts cannot agree on whether the system's results are correct or acceptible, the ...
... formal manner before. • Experts would agree on whether the system's results are correct (or, when there is no single correct result, acceptable). If experts cannot agree on whether the system's results are correct or acceptible, the ...
Page 50
... formal techniques are in less evidence. These techniques evolved to avoid the high cost of iterative coding within the impoverished Software development environments characteristic of time-shared systems a 50 G.S. Kahn and M. Bauer.
... formal techniques are in less evidence. These techniques evolved to avoid the high cost of iterative coding within the impoverished Software development environments characteristic of time-shared systems a 50 G.S. Kahn and M. Bauer.
Contents
25 | |
45 | |
Development tools | 179 |
Knowledge acquisition and modeling | 231 |
Validation and evaluation | 351 |
Further reading | 417 |
A STRUCTURED BIBLIOGRAPHY | 419 |
LIST OF CONTRIBUTORS | 437 |
AUTHOR INDEX | 441 |
Other editions - View all
Topics in Expert System Design: Methodologies and Tools Giovanni Guida,Carlo Tasso Snippet view - 1989 |
Common terms and phrases
abstract activities AI Magazine application approach Artificial Intelligence attribute backward chaining behavior Breuker Building Expert Systems cognitive complete components Computer concepts conceptual model construction context cycle decision defined described diagnosis domain expert domain knowledge environment example Expert System Design expert system development expert system evaluation expert system technology expertise facilities Figure formal function goal graphical heuristics identified implementation important inductive inference input instance integrated interaction interface 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 performance phase problem solving Proc programming Prolog protocol analysis prototype refinement relations reliability repertory grid represent requirements rule-based rules selection shells software engineering solution specific strategies structure task techniques Topics in Expert types validity values