Representing Uncertain Knowledge: An Artificial Intelligence Approach.

Krause, Paul and Dominic Clark:

ISBN 10: 187151617X / ISBN 13: 9781871516173

Published by Intellect;, 1993

About this Item

277 Seiten; Das hier angebotene Buch stammt aus einer teilaufgelösten wissenschaftlichen Bibliothek und trägt die entsprechenden Kennzeichnungen (Rückenschild, Instituts-Stempel.); Schnitt und Einband sind etwas staubschmutzig; der Buchzustand ist ansonsten ordentlich und dem Alter entsprechend gut. Text in ENGLISCHER Sprache! Sprache: Englisch Gewicht in Gramm: 495.

Seller Inventory # 1594841

Bibliographic Details

• Title: Representing Uncertain Knowledge: An Artificial Intelligence Approach.
• Publisher: Intellect
• Publication Date: 1993
• Language: English
• ISBN 10: 187151617X
• ISBN 13: 9781871516173
• Binding: Broschiert
• Edition: 2nd Edition
• Condition: Gut
• Seller catalogs:
  • 202 Informatik & EDV

About this title

Synopsis

This book identifies the central role of managing uncertainty in AI and expert systems and provides a comprehensive introduction to different aspects of uncertainty and the rationales, descriptions (through worked examples), advantages and limitations of the major approaches that have been taken.The book introduces and describes the main ways in which uncertainty can occur and the importance of managing uncertainty for the production of intelligent behaviour in AI and its associated technologies of knowledge-based systems. It also describes the rationale, advantages and limitations of the major representational approaches (both quantitative and symbolic) that have been employed in AI systems and provides a worked illustration of each method. Finally, the book summarises the significant themes that have emerged from applications and the research literature and identifies current and future directions.The book, the first to concentrate wholly on this specific area of Artificial Intelligence, is aimed primarily at researchers and practitioners involved in the design and implementation of expert systems, other knowledge-based systems and cognitive science. It will also be of value to students of computer science, cognitive science, psychology and engineering with an interest in AI or decision support systems. While a technical book, technical details are presented in appendices, allowing the text to be read continuously by nontechnical readers.(abstract)This book assigns the central role of managing uncertainty to AI and expert systems while providing a comprehensive introduction to different aspects of uncertainty. The rationales, advantages and limitations of the major approaches to managing and reasoning under uncertainty are described using worked examples.