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 12
... inputs and outputs, and activities involved. - Definition of the logical relationships between components (precedences, preconditions, constraints, options, etc.). - Definition of the temporal relationships between components (execution ...
... inputs and outputs, and activities involved. - Definition of the logical relationships between components (precedences, preconditions, constraints, options, etc.). - Definition of the temporal relationships between components (execution ...
Page 13
... inputs to the first phase of the life cycle. Each phase of the life cycle is defined and illustrated in detail in the ... input the results of the opportunity analysis and starts with the study of the selected problem area, focusing on a ...
... inputs to the first phase of the life cycle. Each phase of the life cycle is defined and illustrated in detail in the ... input the results of the opportunity analysis and starts with the study of the selected problem area, focusing on a ...
Page 15
... input the plausibility report produced in Phase 1, and starts with the identification of the goals and type of the demonstrator to be developed. Then, from the whole problem to be solved, a limited subproblem is selected, on which the ...
... input the plausibility report produced in Phase 1, and starts with the identification of the goals and type of the demonstrator to be developed. Then, from the whole problem to be solved, a limited subproblem is selected, on which the ...
Page 16
... input the relevant parts of the revised and extended version of the plausibility report produced in Phase 2. At the beginning a wide knowledge acquisition activity is performed in order to identify an appropriate conceptual model for ...
... input the relevant parts of the revised and extended version of the plausibility report produced in Phase 2. At the beginning a wide knowledge acquisition activity is performed in order to identify an appropriate conceptual model for ...
Page 17
... input the relevant parts of the plausibility report refined in Phase 3, and starts with a detailed technical analysis of the real operating environment, which often imposes severe constraints to hardware and software tools to be used ...
... input the relevant parts of the plausibility report refined in Phase 3, and starts with a detailed technical analysis of the real operating environment, which often imposes severe constraints to hardware and software tools to be used ...
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