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 87
Page 15
... domain experts and users; - it is still embedded in the development environment and it is not engineered and optimized (it is generally not efficient and reliable as requested). In addition to the prototype, the outputs of this phase ...
... domain experts and users; - it is still embedded in the development environment and it is not engineered and optimized (it is generally not efficient and reliable as requested). In addition to the prototype, the outputs of this phase ...
Page 16
... domain experts and users. The prototype is then evaluated with reference to the validation and acceptance criteria defined in Phase 1. As outlined above, the prototype is generally a completely different system from the demonstrator ...
... domain experts and users. The prototype is then evaluated with reference to the validation and acceptance criteria defined in Phase 1. As outlined above, the prototype is generally a completely different system from the demonstrator ...
Page 27
... DOMAIN David S. PRERAU Computer and Intelligent Systems Laboratory GTE Laboratories Inc. Waltham, MA, USA 1. INTRODUCTION In this paper, we will discuss techniques by which the knowledge engineering ... EXPERT SYSTEM DOMAIN 1. INTRODUCTION.
... DOMAIN David S. PRERAU Computer and Intelligent Systems Laboratory GTE Laboratories Inc. Waltham, MA, USA 1. INTRODUCTION In this paper, we will discuss techniques by which the knowledge engineering ... EXPERT SYSTEM DOMAIN 1. INTRODUCTION.
Page 28
... expert system technology but must also evaluate and rank potential domains to select the best available application. This paper will examine the process of domain selection for an expert system and will detail a set of attributes that ...
... expert system technology but must also evaluate and rank potential domains to select the best available application. This paper will examine the process of domain selection for an expert system and will detail a set of attributes that ...
Page 29
... domain is selected. The attribute list can used to to justify the decision. Although the above methodology pertains to the situation where domain selectors are ... expert system domain 29 3. DESIRED PROPERTIES OF AN EXPERT SYSTEM DOMAIN.
... domain is selected. The attribute list can used to to justify the decision. Although the above methodology pertains to the situation where domain selectors are ... expert system domain 29 3. DESIRED PROPERTIES OF AN EXPERT SYSTEM DOMAIN.
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