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 91
Page 10
... knowledge acquisition tools, project management tools, knowledge base verification and refinement techniques, etc.) and promote the development of new OneS. Turning now to the core problem of designing a life cycle for expert systems ...
... knowledge acquisition tools, project management tools, knowledge base verification and refinement techniques, etc.) and promote the development of new OneS. Turning now to the core problem of designing a life cycle for expert systems ...
Page 15
... knowledge base. The knowledge base is developed in an incremental way through iterations over the following main steps: - knowledge elicitation from the experts; - knowledge coding and loading in the knowledge base; - verification and ...
... knowledge base. The knowledge base is developed in an incremental way through iterations over the following main steps: - knowledge elicitation from the experts; - knowledge coding and loading in the knowledge base; - verification and ...
Page 16
... knowledge base, are defined, and the tools to be utilized in the development of the prototype are selected. Three main classes of possibilities exist here: - adopting a low-level general purpose programming language; - adopting a high ...
... knowledge base, are defined, and the tools to be utilized in the development of the prototype are selected. Three main classes of possibilities exist here: - adopting a low-level general purpose programming language; - adopting a high ...
Page 35
... knowledge needed may be beyond the state of the art in knowledge base size. • The amount of knowledge required by the task is large enough to be nontrivial. If it is too small, the task may be more amenable to another approach, such as ...
... knowledge needed may be beyond the state of the art in knowledge base size. • The amount of knowledge required by the task is large enough to be nontrivial. If it is too small, the task may be more amenable to another approach, such as ...
Page 48
... knowledge base. Applications differ widely in the degree to which the problem solver contains domain specific information. In this paper, we use "knowledge base" to refer to a domain model of factual or process information. We assume ...
... knowledge base. Applications differ widely in the degree to which the problem solver contains domain specific information. In this paper, we use "knowledge base" to refer to a domain model of factual or process information. We assume ...
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