Artificial Intelligence: Structures and Strategies for Complex Problem Solving (6th Edition) | 
| Author: George F. Luger
Publisher: Addison Wesley
Category: Book
List Price: $109.40
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Avg. Customer Rating:  10 reviews
Sales Rank: 101359
Media: Hardcover
Edition: 6
Number Of Items: 1
Pages: 784
Shipping Weight (lbs): 2.9
Dimensions (in): 9.2 x 7.5 x 1.2
ISBN: 0321545893
Dewey Decimal Number: 006.3
EAN: 9780321545893
ASIN: 0321545893
Publication Date: March 7, 2008
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Book Description
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 representation AI algorithms and data structures in LISP and PROLOG Production systems, blackboards, and meta-interpreters including planers, rule-based reasoners, and inheritance systems. Machine-learning including ID3 with bagging and boosting, explanation based learning, PAC learning, and other forms of induction Neural networks, including perceptrons, back propogation, Kohonen networks, Hopfield networks, Grossberg learning, and counterpropagation. Emergent and social methods of learning and adaptation, including genetic algorithms, genetic programming and artificial life. Object and agent-based problem solving and other forms of advanced knowledge representation
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| Customer Reviews: Read 5 more reviews...
good mention of Hidden Markov Models April 20, 2008
3 out of 5 found this review helpful
One distinguishing feature of the 6th edition is the prominent place given to Hidden Markov Models. Indeed, one might have asked for these to have been equally prominent in earlier editions. For several (>10) years, HMMs have been successfully used in various practical applications. Above all, in Automatic Speech Recognition. To often correctly infer the word or phrase that was uttered. The models have made ASRs prominent and for the most part, practical in being used in mass consumer applications. But HMMs in those contexts were not often considered AI per se. Here, the text moves HMM squarely into view, as a valid and vital technique for AI.
Not that the text is restricted to this, of course. It still has a broad introductory coverage of major AI topics. Consider the predicate calculus. Or stochastic methods to infer meaning. [You might consider HMM to be a special type of stochastic method.]
Perhaps the best summary of the book is that it seems attuned to practical applications of AI. The algorithm descriptions and suggested usages aid the porting to contexts where you do not necessarily need the full panoply of AI. The hard AI problems you might leave to others. You can treat this entire text as a good summation of powerful computational algorithms.
 Outdated November 18, 2007
6 out of 8 found this review helpful
In 2004 nobody should be wirting AI books like this one. Lisp and Prolog are still good languages, but people have already realized that there is no "AI language" -- and most AI researchers today use C++, Java, Matlab and other languages. AI is not about "Lisp and Prolog" at all!
The book does not give a good overview of AI today. See Russel&Norvig, for example -- it was published ealier than this one, but is more up to date.
If you want to learn AI, get Russel&Norvig's book (second edition) to have a feeling of what was around in 2000. It's still not what's going on today, but it's much better than Luger's book. Then, choose an area and start reading lots of books and papers about it. For example, if you choose "Machine Learning", there are tons of good books. The same for other areas...
 Superficial and unclear May 26, 2005
6 out of 11 found this review helpful
Trying to gather the greatest audience possible, this book is superficial, completly unclear and boring. Why? Topics are quickly introduced, concepts are rarely analized deeply, it's more discorsive than formal. With so many subjects of AI in the same book not enough space can be given to all of them, so most of the chapters are lists of important algorithms or concepts, barely explained. Do you want to verify it? See the table of contents and the number of pages, and try to see how much space can be given to every point... not enough.
 Fantastic Introduction to AI January 6, 2005
6 out of 8 found this review helpful
This book really stands out among the AI texts (I've read 4 others). First, the language is clear and simple enough for undergrads to grasp. Second, there are consistent examples that pervade the text to help the reader apply each method to an established problem. Third, the explanations of algorithms/structures are crafted and phrased to TEACH, not merely to summarize a bunch of material for reference purposes. Finally, the programming chapters allow the student to realize the material, and really think about the problems by implementing them and hashing out the details.
I cannot complain about any lack of depth - the length already exceeds 900 pages. To those that desire more, look into academic journals - this is an intro. Moreover, robotics, vision, neural nets, and other topics already have their own "forked" research fields, with textbooks of comparable length focusing on those topics alone!
Enjoy! This text is sure to get you started!
 this book not cover much July 14, 2003
9 out of 17 found this review helpful
I bought this book for my introduction course in AI. I feel that this book has lack of somethings which are very important, neural networks, and Ai and robotics to name a few. I found that the text is very hard to understand. Again he didn't use enough example to explain some of the topics. I am lost reading this book. The book is not well structured and turned me bored after 30 minutes reading it. The reason are, AI term definations are not included as other book do, few visual diagrams, objective is not well defined. Once again, he didn't include introduction/review of what we acpect to learn of each of every chapters. Reading it is like reading a "white bible". Only plain text and unprofessional layout. This book discorage me reading it. I think i should buy other book that have a wider coverage topics in AI and yet easy to understand, consistent with my AI course syllibus and yet easy for my eyes.
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