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 51
Page 11
... inference engine becomes fuzzy. - Concrete experience with the design and development of real-size projects is still limited. - View of computer systems as problem solvers; software products development is, thus, centered around the ...
... inference engine becomes fuzzy. - Concrete experience with the design and development of real-size projects is still limited. - View of computer systems as problem solvers; software products development is, thus, centered around the ...
Page 48
... inference engine, typically provides an approach to interpreting or using the contents of a knowledge base. Applications differ widely in the degree to which the problem solver contains domain specific information. In this paper, we use ...
... inference engine, typically provides an approach to interpreting or using the contents of a knowledge base. Applications differ widely in the degree to which the problem solver contains domain specific information. In this paper, we use ...
Page 70
... inference mechanisms and control strategies – implementation of knowledge-representation formalisms — complex data objects to be manipulated — possibly the understanding of some limited form of natural language On the other hand, in ...
... inference mechanisms and control strategies – implementation of knowledge-representation formalisms — complex data objects to be manipulated — possibly the understanding of some limited form of natural language On the other hand, in ...
Page 71
... inference mechanism (depth—first backward chaining) be used. All this is unnecessarily limiting. (2) Prolog's automatic back tracking facility releaves the user from programming backtracking explicitly. This is considered important ...
... inference mechanism (depth—first backward chaining) be used. All this is unnecessarily limiting. (2) Prolog's automatic back tracking facility releaves the user from programming backtracking explicitly. This is considered important ...
Page 75
... inference mechanism. Prolog's strategy is backward chaining in the depth-first fashion. Accordingly, in our example the reasoning started with the goal a, and ended when the fact e was encountered. As Prolog is a general programming ...
... inference mechanism. Prolog's strategy is backward chaining in the depth-first fashion. Accordingly, in our example the reasoning started with the goal a, and ended when the fact e was encountered. As Prolog is a general programming ...
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