Much has changed since the early editions of Artificial Intelligence were published. To reflect this the introductory material of this fifth edition has been substantially revised and rewritten to capture the excitement of the latest developments in AI work. Artificial intelligence is a diverse field. To ask the question "what is intelligence?" is to invite as many answers as there are approaches to the subject of artificial intelligence. These could be intelligent agents, logical reasoning, neural networks, expert systems, evolutionary computing and so on. This fifth edition covers all the main strategies used for creating computer systems that will behave in "intelligent" ways. It combines the broadest approach of any text in the marketplace with the practical information necessary to implement the strategies discussed, showing how to do this through Prolog or LISP programming.
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[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.
About the Author:
George Luger is currently a Professor of Computer Science and Psychology at the University of New Mexico. His research interests include modeling human intelligence and building intelligent control systems. He received his PhD at the University of Pennsylvania and has worked as a research fellow at the University of Edinburgh.
William Stubblefield is currently a Senior Member of Technical Staff at Sandia National Laboratories. His research interests include intelligent manufacturing systems, human-computer interaction, and computational models of metaphor and analogy. He received his PhD at the University of New Mexico and has worked as a visiting professor at Dartmouth College.
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