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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.
- Sales Rank: #896887 in Books
- Published on: 2004-10-10
- Original language: English
- Number of items: 1
- Dimensions: 9.14" h x 1.90" w x 7.64" l,
- Binding: Hardcover
- 928 pages
From the Back Cover
[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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Excerpt. © Reprinted by permission. All rights reserved.
What we have to learn to do
we learn by doing. . .
— ARISTOTLE, Ethics
I was very pleased to be asked to produce a fourth edition of our artificial intelligence book. It is a compliment to the earlier editions, started more than a decade ago, that our approach to Al has been widely accepted. It is also exciting that, as new developments in the field emerge, we are able to present much of it in each new edition. We thank our readers, colleagues, and students for keeping our topics relevant and presentation up to date.
Many sections of the earlier editions have endured remarkably well, including the presentation of logic, search algorithms, knowledge representation, production systems, machine learning, and the programming techniques developed in LISP and PROLOG. These remain central to the practice of artificial intelligence, and required a relatively small effort to bring them up to date. However, several sections, including those on natural language understanding, reinforcement learning, and reasoning under uncertainty, required, and received, extensive reworking. Other topics, such as emergent computation, case-based reasoning, and model-based problem solving, that were treated cursorily in the first editions, have grown sufficiently in importance to merit a more complete discussion. These changes are evidence of the continued vitality of the field of artificial intelligence.
As the scope of the project grew, we were sustained by the support of our publisher, editors, friends, colleagues, and, most of all, by our readers, who have given our work such a long and productive life. We were also sustained by our own excitement at the opportunity afforded: Scientists are rarely encouraged to look up from their own, narrow research interests and chart the larger trajectories of their chosen field. Our publisher and readers have asked us to do just that. We are grateful to them for this opportunity.
Although artificial intelligence, like most engineering disciplines, must justify itself to the world of commerce by providing solutions to practical problems, we entered the field of AI for the same reasons as many of our colleagues and students: we want to understand and explore the mechanisms of mind that enable intelligent thought and action. We reject the rather provincial notion that intelligence is an exclusive ability of humans, and believe that we can effectively investigate the space of possible intelligences by designing and evaluating intelligent artifacts. Although the course of our careers has given us no cause to change these commitments, we have arrived at a greater appreciation for the scope, complexity, and audacity of this undertaking. In the preface to our earlier editions, we outlined three assertions that we believed distinguished our approach to teaching artificial intelligence. It is reasonable, in writing a preface to this fourth edition, to return to these themes and see how they have endured as our field has grown.
The first of these goals was to "unify the diverse branches of AI through a detailed discussion of its theoretical foundations." At the time we adopted that goal, it seemed that the main problem was reconciling researchers who emphasized the careful statement and analysis of formal theories of intelligence (the neats) with those who believed that intelligence itself was some sort of grand hack that could be best approached in an application-driven, ad hoc manner (the scruffies). That simple dichotomy has proven far too simple. In contemporary AI, debates between neats and scruffies have given way to dozens of other debates between proponents of physical symbol systems and students of neural networks, between logicians and designers of artificial life forms that evolve in a most illogical manner, between architects of expert systems and case-based reasoners, and finally, between those who believe artificial intelligence has already been achieved and those who believe it will never happen. Our original image of AI as frontier science where outlaws, prospectors, wild-eyed prairie prophets and other dreamers were being slowly tamed by the disciplines of formalism and empiricism has given way to a different metaphor: that of a large, chaotic but mostly peaceful city, where orderly bourgeois neighborhoods draw their vitality from diverse, chaotic, bohemian districts. Over the years we have devoted to the different editions of this book, a compelling picture of the architecture of intelligence has started to emerge from this city's structure, art, and industry.
Intelligence is too complex to be described by any single theory; instead, researchers are constructing a hierarchy of theories that characterize it at multiple levels of abstraction. At the lowest levels of this hierarchy, neural networks, genetic algorithms and other forms of emergent computation have enabled us to understand the processes of adaptation, perception, embodiment, and interaction with the physical world that must underlie any form of intelligent activity. Through some still partially understood resolution, this chaotic population of blind and primitive actors gives rise to the cooler patterns of logical inference. Working at this higher level, logicians have built on Aristotle's gift, tracing the outlines of deduction, abduction, induction, truth-maintenance, and countless other modes and manners of reason. Even higher levels of abstraction, designers of expert systems, intelligent agents, and natural language understanding programs have come to recognize the role of social processes in creating, transmitting, and sustaining knowledge. In this fourth edition, we have touched on all levels of this developing hierarchy.
The second commitment we made in the earlier editions was to the central position of "advanced representational formalisms and search techniques" in AI methodology. This is, perhaps, the most controversial aspect of our previous editions and of much early work in AI, with many researchers in emergent computation questioning whether symbolic reasoning and referential semantics have any role at all in thought. Although the idea of representation as giving names to things has been challenged by the implicit representation provided by the emerging patterns of a neural network or an artificial life, we believe that an understanding of representation and search remains essential to any serious practitioner of artificial intelligence. More importantly, we feel that the skills acquired through the study of representation and search are invaluable tools for analyzing such aspects of non-symbolic AI as the expressive power of a neural network or the progression of candidate problem solutions through the fitness landscape of a genetic algorithm. Comparisons, contrasts, and a critique of the various approaches of modern AI are offered in Chapter 16.
The third commitment we made at the beginning of this book's life cycle, to "place artificial intelligence within the context of empirical science," has remained unchanged. To quote from the preface to the third edition, we continue to believe that AI is not
. . . some strange aberration from the scientific tradition, but . . . part of a general quest for knowledge about, and the understanding of intelligence itself. Furthermore, our AI programming tools, along with the exploratory programming methodology . . . are ideal for exploring an environment. Our tools give us a medium for both understanding and questions. We come to appreciate and know phenomena constructively, that is, by progressive approximation.
Thus we see each design and program as an experiment with nature: we propose a representation, we generate a search algorithm, and then we question the adequacy of our characterization to account for part of the phenomenon of intelligence. And the natural world gives a response to our query. Our experiment can be deconstructed, revised, extended, and run again. Our model can be refined, our understanding extended.
New with This EditionI, George Luger, am the sole author of the fourth edition. Although Bill Stubblefield has moved on to new areas and challenges in computing, his mark will remain on the present and any further editions of this book. In fact this book has always been the product of my efforts as Professor of Computer Science at the University of New Mexico together with those of my professional colleagues, graduate students, and friends: the members of the LTNM artificial intelligence community, as well as of the many readers that have e-mailed me comments, corrections, and suggestions. The book will continue this way, and to reflect this community effort, I will continue using the prepositions we and us when presenting material. Individual debts in the preparation for this fourth edition are listed in the acknowledgement section of this preface.
We revised many sections of this book to recognize the growing importance of agent-based problem solving as an approach to AI technology. In discussions of the foundations of AI we recognize intelligence as physically embodied and situated in a natural and social world context. Apropros of this, we present in Chapter 6 the evolution of AI representational schemes from associative and early logic-based, through weak and strong method approaches, including connectionist and evolutionary/emergent models, to situated and social approaches to AI. Chapter 16 contains a critique of each of these paradigms.
In creating this fourth edition, we considered all topics presented earlier and brought them into a modern perspective. In particular, we added a reinforcement learning soon to Chapter 9. Algorithms for reinforcement learning, taking cues from an environment to establish a policy for state change, including temporal difference and Q-learning, are presented.
Besides our previous analysis of data-driven and goa-driven rule-based systems, Chapter 7 no...
Most helpful customer reviews
20 of 23 people found the following review helpful.
An excellent book for an intro to AI
By Todd Ebert
This book is actually a follow up to Luger and Stubblefield's older book "AI and the Design of Expert Systems". Being somewhat dated in both title and content, this book serves as its resurrection. Both books are excellent in providing a basic introduction to AI. They contain a number of problems and provide just enough infromation on each topic to give the reader the general idea and a sense of having learned something substantial (this is always the danger when writing a book that surveys a variety of interrelated fields). Another strength of the book is its ability to make connections between the different areas of AI. For example, when discussing knowledge representation, they make sure to draw connections with it and logic as well as natural language processing.
15 of 17 people found the following review helpful.
Good For Beginners in AI
By Gautam Renjen
This is a very good book for anyone wanting to get an insight. Good for the first college course in AI too. It introduces the different areas of AI quite well, and develops logic before doing that. Prolog and LISP are also introduced.
The only reason I wouldn't give this book 5 stars is because
1) The Prolog and LISP features aren't all that great. They could have done better than just explaining what they did.
2) There was very little or almost no depth in the material covered. I wanted to go on reading more about the advanced features, but that never happened. So, I had to go to the library and look for something there.
But a great book for a college course. I wouldn't recommend this for a Grad course in CS...A grad student should be knowing beyond what this book covers.
14 of 16 people found the following review helpful.
This is a good book for the beginners.
By Amit Konar
The book presents many aspects like predicate logic, state space search,knowledge representation, natural language understanding, machine learning and specially programming in both LISP and PROLOG.I specially liked the chapters on learning, natural language understanding and the programming techniques.The book is unique for its presentation style, simplicity and illustrations. It must be on the desk of anyone interested to join the disciplines of AI.
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