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
Results 1-5 of 89
Page v
... decision making, planning, design, control, supervision, diagnosis, etc. These expectations have pushed the development of a number of projects devoted to experiment expert system technology in real-size applications of concrete ...
... decision making, planning, design, control, supervision, diagnosis, etc. These expectations have pushed the development of a number of projects devoted to experiment expert system technology in real-size applications of concrete ...
Page 10
... decisions about project feasibility and appropriateness of the technology are much more complex and critical than in a traditional software engineering environment. Thus, a clear separation between the preliminary study, devoted to ...
... decisions about project feasibility and appropriateness of the technology are much more complex and critical than in a traditional software engineering environment. Thus, a clear separation between the preliminary study, devoted to ...
Page 12
... decisions and to make them explicit through appropriate definitions, namely: - Identification of a suitable decomposition of the production process into components, possibly at several levels of detail and abstraction. - Precise ...
... decisions and to make them explicit through appropriate definitions, namely: - Identification of a suitable decomposition of the production process into components, possibly at several levels of detail and abstraction. - Precise ...
Page 13
... decisions, developing a draft system design, and preparing a draft project plan. The output of this phase is a technical document, called plausibility report, which illustrates the activities done and the obtained results, suggests ...
... decisions, developing a draft system design, and preparing a draft project plan. The output of this phase is a technical document, called plausibility report, which illustrates the activities done and the obtained results, suggests ...
Page 14
... decisions proposed in Phase l; - collecting concrete reactions, suggestions, and critics from the users, so as to refine the identification of requirements and the definition of system specifications outlined in Phase l; - gaining ...
... decisions proposed in Phase l; - collecting concrete reactions, suggestions, and critics from the users, so as to refine the identification of requirements and the definition of system specifications outlined in Phase l; - gaining ...
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 |
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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