Artificial Intelligence: Structures and Strategies for Complex Problem Solving - Couverture rigide

Luger, George F.; Stubblefield, William A.

Couverture rigide

ISBN 10 : 0805311963 ISBN 13 : 9780805311969

Editeur : Benjamin Cummings, 1997

Synopsis

Book by Luger George F Stubblefield William A

A propos du livre

Quatrième de couverture :

[Shelving Category: Artificial Intelligence]

"One of the few books on the market that covers all the topics I have included in my course for the past 10 years." Bruce Maxim, University of Michigan �?? Dearborn

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

"Excellent additions and improvements. I will use the 5th edition in my introduction and advanced AI courses." Peter Funk, M�?�lardalen 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

Can machines think like people? This question is the driving force behind Artificial Intelligence, but it is only the starting point of this ever-evolving, exciting discipline. AI uses different strategies to solve the complex problems that arise wherever computer technology is applied, from those areas pertaining to perception and adaptation (neural networks, genetic algorithms) to the fields of intelligent agents, natural language understanding and stochastic models.

George Luger examines complex problem solving techniques while demonstrating his enthusiasm and excitement for the study of intelligence itself. He shows how to use a number of different software tools and techniques to address the many challenges faced by today�??s computer scientists.

New to this edition

·         Brand new chapter which introduces the stochastic methodology.

·         Extended material in many sections addresses the continuing importance of agent-based problem solving and embodiment in AI technology.

·         Presentation of issues in natural language understanding, including sections on stochastic methods for language comprehension; Markov models; CART trees; mutual information clustering; and statistic based parsing.

·         Further discussion of the AI endeavor from the perspectives of philosophy, psychology, and neuro-psychology.

Artificial Intelligence: Structures and Strategies for Complex Problem Solving is ideal for a one or two semester university course on AI, as well as an invaluable reference for researchers in the field or practitioners wishing to employ the power of current AI techniques in their work.

After receiving his PhD from the University of Pennsylvania,George Lugerspent five years researching and teaching at the Department of Artificial Intelligence of the University of Edinburgh. He is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico.

 

Présentation de l'éditeur :

Combines the theoretical foundations of intelligent problem-solving with he data structures and algorithms needed for its implementation. The book presents logic, rule, object and agent-based architectures, along with example programs written in LISP and PROLOG.
The practical applications of AI have been kept within the context of its broader goal: understanding the patterns of intelligence as it operates in this world of uncertainty, complexity and change.

The introductory and concluding chapters take a new look at the potentials and challenges facing artificial intelligence and cognitive science.An extended treatment of knowledge-based problem-solving is given including model-based and case-based reasoning.

Includes new material on:Fundamentals of search, inference and knowledge representatioAI algorithms and data structures in LISP and PROLOProduction systems, blackboards, and meta-interpreters including planers, rule-based reasoners, and inheritance systemsMachine-learning including ID3 with bagging and boosting, explanation based learning, PAC learning, and other forms of inductioNeural networks, including perceptrons, back propogation, Kohonen networks, Hopfield networks, Grossberg learning, and counterpropagationEmergent and social methods of learning and adaptation, including genetic algorithms, genetic programming and artificial lifeObject and agent-based problem solving and other forms of advanced knowledge representation
 

À propos de cette édition

• Éditeur: Benjamin Cummings
• Date d'édition: 1997
• ISBN 10: 0805311963
• ISBN 13: 9780805311969
• Reliure: Relié
• Numéro d'édition: 3
• Nombre de pages: 864