Customer Reviews

Artificial Intelligence: Structures and Strategies for Complex Problem Solving

5 star:		(5)
4 star:		(2)
3 star:		(1)
2 star:		(1)
1 star:		(0)

 

 

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.

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.

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.

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!

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.

This book is almost a perfect solution for an introductory AI class, since it presents the chapters on both Prolog and LISP programming. The introduction to those languages is rather detailed, the Prolog chapter takes up about 75 pages while LISP introduction is almost 100 pages long. Without focusing on any particular subject in AI the book tends to be substantial and detailed.

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.

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.

This is the best general AI book I've seen this far. It introduces all the popular branches of AI clearly. If you are serious about AI, you should own this book...