ARTIFICIAL INTELLIGENCE AND THE DESIGN OF EXPERT SYSTEMS

Luger, George F. & William A. Stubblefield

ISBN 10: 0805301399 / ISBN 13: 9780805301397

Edité par Benjamin-Cummings Publishing Company, Subs of Addison Wesley Longman, Inc, Redwood City, CA, 1989

A propos de cet article

Very Good in a Very Good dust jacket. ; The Benjamin/cummings Series in Artificial Intelligence; 1.3 x 9.2 x 7.7 Inches; 660 pages.

N° de réf. du vendeur 48049

Détails bibliographiques

• Titre: ARTIFICIAL INTELLIGENCE AND THE DESIGN OF EXPERT SYSTEMS
• Éditeur: Benjamin-Cummings Publishing Company, Subs of Addison Wesley Longman, Inc, Redwood City, CA
• Date de parution: 1989
• Langue: anglais
• ISBN à 10 chiffres: 0805301399
• ISBN à 13 chiffres: 9780805301397
• Reliure: Hardcover
• Édition: First Edition; First Printing.
• Catalogues du vendeur:
  • misc sale
  • Discounted
  • Other

A propos de ce titre

Synopsis

Book by Luger George F Stubblefield William A

Quatrième de couverture

Artificial Intelligence

Structures and Strategies for Complex Problem Solving, Sixth Edition

by George F Luger

 

This accessible, comprehensive book captures the essence of artificial intelligence -- solving the complex problems that arise wherever computer technology is applied. With his signature enthusiasm, George Luger demonstrates numerous techniques and strategies for addressing the many challenges facing computer scientists today. Diverse topics on this exciting and ever-evolving field range from perception and adaptation using neural networks and genetic algorithms, intelligent agents with ontologies, automated reasoning, natural language analysis, and stochastic approaches to machine learning.

 

This book is ideal for a one - or two-semester university course on AI.

 

New to this edition:

• A new chapter on stochastic approaches to machine learning, including first-prder Bayesian networks, variants of hidden Markov models, inference with Markov random fields and loopy belief propagation.
• Presentation of parameter fitting with expectation maximization learning and structure learning using Markov chain Monte Carlo sampling. Use of Markov decision processes in reinforcement learning.
• Presentation of agent technology and the use of ontologies.
• Natural language processing with dynamic programming (the Earley parser) and other probabilistic parsing techniques including Viterbi.
• A new supplemental programming book is available: AI Algorithms in Prolog, Lisp, and Java™. Available online and in print, this book demonstrates these languages as tools for building many of the algorithms presented throughout Luger's AI book.

"There are many ideas in this area that students often find difficult; the clarity and precision of Luger's exposition is informed by sharp, incisive examples with straightforward graphical components."

-- Joseph Lewis, San Diego State University

 

"The book is a perfect complement to an AI course. It gives readers both an historical point of view and a practical guide to all the techniques. It is THE book I would recommend as an introduction to this field."

-- Pascal Rebreyend, Dalarna University 

 

"The style of writing and comprehensive treatment of the subject matter makes this a valuable addition to the AI literature."

-- Malachy Eaton, University of Limerick

 

George Luger is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico. He received his Ph.D. from the University of Pennsylvania and spent five years researching and teaching at the Department of Artificial Intelligence at the University of Edinburgh.