Artificial Intelligence Books

This book is great for the self taught person with significant insignificant questions. In completing this book, all those questions answer themselves. None of that "cutsie" just great tutorial. Instruction is easily to understand and follow. I've gone through the book a couple of times and discovered something new each time. When I finished with this book, I knew what I was doing and why.

Money well spent on this book.

Straigh-forward writing with no pretension. Some knowledge of 3D graphics vocabulary is helpful but not necessary.

Book is one example from beginning to end; presumably the author. Starts with some pictures and, step-by-detailed-step, ends with an avatar.

The only fault I found is that he doesn't mention Poser in the list of 3D modeling programs for human figures.

If you want to learn how to model a detailed digital human,this is THE book for you! You start out with totally empty viewports,and if you follow the book you'll end up having created a model with an incredible amount of detail.

The author explains in great detail the process of modelling every body part (head,neck,arms,hands,legs,feet and torso) with anatomical references where they're most important.

I wanted a book which I could use as a definitive guide to model a detailed and anatomically correct human body or body part,and I'll look no further when I have to do so. It's also got a clever chapter about modifying the same model to create very different ones, and a good chapter about texturing and UVW unwrapping. Finally, it refers to cloth and hair (somewhat briefly) and,no,it DOESN'T cover rigging. But it does cover, extremely well, human modelling, which is what mr.Brilliant had set out to do,I assume. Very very good!

I used this as a class textbook and it worked fairly well. This is not a single program book so this will work well with whatever program you model with. Although, depending on what you model with, depends on if you need to go out and find plug-ins that will do what he does. The book is really good going through step by step. Although there are some occasions where he leaps forwards ahead with really telling you what to do. Also, sometimes when he gives instructions, there aren't any images to go along with them, so you have to end up guessing what to do.
This is modeling for realism/cinematics and if you want to use this book to model in-game characters, you are out of luck. The was he teaches you to model is extremely high poly (especially in the head). The CD doesn't do much for you, it mainly just has naked pictures of the guy he models on it so you can copy exactly what he does. The book does give good information on the differences between modeling men and women, although it is fairly brief. He does go into UV mapping pretty good as well as modeling hair. The book doesn't, however, go into modeling clothing fairly well, just a short chapter. The book also doesn't even mention rigging, which I think is a crucial part in character modeling.

Given that you are already familiar with some type of modeling software, this book is the best resource you could have! The non-software specific approach of the book keeps it focused on the concepts of creating a good model rather than the user interface. Every detail of the body is covered with step-by-step diagrams, and a lot of it focuses on creating a topology that is animatable and will subdivide correctly (ie the mesh is mostly in quads). It also covers texturing, rigging, facial animation, both modeled and simulated hair, and simple clothing. The book discusses anatomy to the extent that it is needed to create a realistic model, and uses those concepts to demonstrate how the male model you create can be changed into a female, or into a fantasy character that looks completely different.

One thing that did make it a little difficult to use was that in the screenshots, the mesh was transparent and therefore you couldn't tell whether vertices were at the front or the back of the model. More screenshots with an opaque mesh would have made it easier to see the topology.

Overall, the explanations are concise and makes the task seem efficient, easy, and fun.

"This book is intended for researchers, practicing engineers, and students who wish to understand and / or develop fingerprint-based recognition systems." So the introduction, and right they are. The authors have chosen a more informal style, but still give some of the Mathematics necessary. The references are extensive and ultimately the reader will have to go to them to make a contribution.

I would recommend buying this book with "Automatic Fingerprint Recognition Systems" by Ratha and Bolle (ed.) It would be hard for me to make a decision between these two books.

Lo recomiendo si desean implementar nuevos algoritmos para reconocimiento de huellas dactilares y/o una revision a profundidad de como funcionan los metodos existentes.

Imprescindible concimiento de gradientes y algoritmos digitales en el tratamiento de imagenes.. para comprender el contenido.

This book first describes the different biometric systems that exist. It describes different methods of acquiring the fingerprints and the advantages and drawbacks from each method. Then it handles the different analysis methods for fingerprints in depth. The matching algorithms that are known with their evaluation is handled throughly with their indexing and classification methods. It discusses methods for fingerprint evaluation with the databases of NIST on DVD and the software sfinge for artificially making the fingerprints.

The part that is really new is the multimodal biometric system and performance measures of these systems. It also handles the individuality of fingerprints themselves and finally it gives an overview of methods for attacking fingerprints systems (denial of service, fake fingers, trojan horses, replay but also the electronic storage systems behind them). If you are in biometrics and also in forensic science, you certainly should consider reading this book.

This book is a "must". Very useful for my research on Biometrics and Multibiometrics Systems. No wonder this book received the 2003 PSP Award (Computer Science) from the Association of American Publishers.

The applications of Biometrics technology range from federated identity management to digital rights management, time and attendance, background checking, and homeland security. None of the biometric characteristics has received more attention till date than fingerprints. Fingerprint recognition is the most cost effective and accurate biometrics, which is evident from its rapid deployment in the widest range of security applications.

This book contains a plethora of information on every aspect of fingerprint recognition technology - introduction to biometrics and fingerprints, fingerprint sensing devices, feature extraction, matching, classification, synthetic fingerprint generation, multimodal systems, secure design, fingerprint individuality - you name it. The DVD accompanying this book will save you a lot of money and trouble of collecting your own data to test your algorithms.

The authors of this book - Davide Maltoni, Dario Maio, Salil Prabhakar, and Anil K. Jain - are undoubtedly some of the most well known and respected experts in the world on the topic of fingerprint recognition. It is no surprise that such extraordinary researchers have produced such an exceptional masterpiece.

Whether you are an inventor, developer, practitioner, forensic specialist, or system manager in this field looking for an excellent reference or just a novice looking for basic information on biometrics and fingerprint recognition technology, you must read this outstanding book.

Developing speech applications is not easy to master. Even with VoiceXML becoming more widely adopted, there are a lot of intricacies that that a developer must understand. This book will provide you with a solid foundation to become an effective speech application developer.

The book did very well in presenting the limitations of the current speech recognition technology (dialog design, large vocabularies, promtp design, etc.) and made suggestions on how to overcome such problems in specific situations.

When this book came out a couple of years ago it was the first and only book on designing speech recognition systems. It was very valuable then, but now more books are available that cover the same information and more, for a lot less money.

This book is simply the best for your bookcase if you are a voice dialogue designer. I would strongly recommend it to novice and expert alike, especially for those learning VoiceXML for the first time, or working with it day to day.

Grounded in hours of human-computer experiments, and a multi-disciplinary approach to user interface design - this book is a rare combination of a careful ear for human language and dialogue, extensive engineering experience, and pragmatic knowledge of the strengths and limitations of current voice recognition technology.

The second edition has brought it bang up-to-date. It cuts through the hype that has always surrounded each successive generation of voice technology - focussing always on the building of robust useable interfaces which work with the user rather than against them.

This book is so well organized and articulated it's bound to be of value to anyone doing ASR application development. My own experience in voice response runs from end-user representative to application designer and I found every chapter enlightening. In this second edition of Bruce and David's ASR style guide, I believe the end users will find the new sections on voice portals and managing your voice talent of particular interest. And all users should take particular note of the expanded discussions of usability testing and performance reporting.

I found the first version of How to Build a Speech Recognition Application so useful that I actually took the time to compared the new edition, page for page, with the original. That was a relatively easy task, because the authors retained the original section numbering wherever possible. My comparison showed that the original guidelines have been substantially updated, based on continuing research and the hands-on experiences of both the authors and other acknowledged experts. In addition, I believe the new sections and expanded discussions of critical design considerations are going to prove valuable to both novice and seasoned developers.

In short, developing effective telephony dialogues is a complex, rapidly evolving and downright expensive task. Given that reality, every development team ought to have at least one copy of this landmark style guide.

Author, Bruce Balentine's goal with How to Build a Speech Recognition Application is to produce the Strunk and White of speech recognition. An electronic musician and composer, Balentine was a pioneer in the speech recognition field. The text's examination of the problems of navigation from human perception to machine recognition give comprehension to even the layman. The book is well organized and structured with the +,/,- system which allows a novice to follow. According to linguist, Dr. John White, the first chapter could stand alone as a treatise on the dynamics of the human speech interface with the machine.

This is a very neat book that gets you started with broad knowledge
about what you can do with the overwhelming World Wide Web.
If you are curious about what are behind those search engines and
how can these "things" get you stuck in front of
your computer around the clock, this is the book for you.
It not only tells you how these "things" work,
but also calms you a little bit by telling you that
those guys who developed these "things" REALLY tried hard to
get you what you want and in the meantime save you some time :)
The best part is that you don't need to know many theories and
you still get some sense about the devils who drive these engines.
If you are a professional who wants to know where to read about the
"know how", this book could be a good starting point.
It not only gives you a good survey of what is going on,
but also provides you with 286 references that guide you
to what you need to know next.
If you are a graduate student who wants to start a project
on the subject, this book could save you some time.
It takes you only couples of hours to scan through it.
By the end, you would probably know where to dig deeper or
you might get burnt and choose a different subject.
One thing I was wondering was that the authors didn't go further
in many aspects. Some subsections have only four to five sentences.
These could be spaces to extend.

The fields of Data Mining and Information Retrieval are incredibly complex and deep subject areas even when they are divorced from the structure of the world wide web. If the world wide web is viewed as a giant data base, it is without a doubt the most complex data base which has ever existed.

A major problem is getting a grasp on the synthesis of these three fields, DM, IR, and WWW technology. Even current research in DM is distributed among gropus of people with such diverse backgrounds effective communication of research results across groups is extremely difficult.

This book has taken the major concepts from these three fields and organized them in outline form. The outline cuts just deep enough to be meaningful and never too deeply to "lose" the reader. For the serious student, this book provides a Christmas tree on which other books can hang like ornaments.

Obviously, I think very highly of this book. It is not the "be all and the end all", but it fills an important niche. ... Almost limits it to library and other institutional purchases. Which is a shmae because I'm sure every worker in WWWIR&DM would like to have a copy on their shelves.

BTW, the bibliography isn't bad either, and, includes many www URLs, a must for any truly useful bibliography in todays environment. The search engines just aren't good enough yet to give you all the URLs you need. But, then, improving them is part of why there is so much active research in WWWDM&IR.

Feel free to write the author of this review (Dr. John Aiken, PhD)...

This is a very neat book that gets you started with broad knowledge
about what you can do with the overwhelming World Wide Web.
If you are curious about what are behind those search engines and
how can these "things" get you stuck in front of
your computer around the clock, this is the book for you.
It not only tells you how these "things" work,
but also calms you a little bit by telling you that
those guys who developed these "things" REALLY tried hard to
get you what you want and in the meantime save you some time :)
The best part is that you don't need to know many theories and
you still get some sense about the devils who drive these engines.
If you are a professional who wants to know where to read about the
"know how", this book could be a good starting point.
It not only gives you a good survey of what is going on,
but also provides you with 286 references that guide you
to what you need to know next.
If you are a graduate student who wants to start a project
on the subject, this book could save you some time.
It takes you only couples of hours to scan through it.
By the end, you would probably know where to dig deeper or
you might get burnt and choose a different subject.
One thing I was wondering was that the authors didn't go further
in many aspects. Some subsections have only four to five sentences.
These could be spaces to extend.

This is a very neat book that gets you started with broad knowledge
about what you can do with the overwhelming World Wide Web.
If you are curious about what are behind those search engines and
how can these "things" get you stuck in front of
your computer around the clock, this is the book for you.
It not only tells you how these "things" work,
but also calms you a little bit by telling you that
those guys who developed these "things" REALLY tried hard to
get you what you want and in the meantime save you some time :)
The best part is that you don't need to know many theories and
you still get some sense about the devils who drive these engines.
If you are a professional who wants to know where to read about the
"know how", this book could be a good starting point.
It not only gives you a good survey of what is going on,
but also provides you with 286 references that guide you
to what you need to know next.
If you are a graduate student who wants to start a project
on the subject, this book could save you some time.
It takes you only couples of hours to scan through it.
By the end, you would probably know where to dig deeper or
you might get burnt and choose a different subject.
One thing I was wondering was that the authors didn't go further
in many aspects. Some subsections have only four to five sentences.
These could be spaces to extend.

This is a nice introductory book, short and well written. Don't expect any details, it is only an outline. It does a good job of covering terminology, and suggesting additional reading, but it isn't a primary resource itself.

The book is divided into 3 sections. The first is on 'information retrieval' (IR), the second on data mining, and the third describes a 'case study.'

According to the authors, IR is engaged in storage, retrieval, organization and display of unstructured or ambiguous file structures. Research is currently engaged in classifying, filtering, modeling, query design and user interface issues. The key question for IR is 'relevance' assessment. Each topic gets at least a few paragraphs, some a few pages.

The authors differentiate data mining from IR in terms of focus. A data mining project is designed specifically for finding hidden structure (whatever that means), while IR might be characterized as the 'quick and dirty query.' This is a bit confusing, but the emphasis on terminology makes it unimportant. Most of the data mining section is a review of various measures used to determine the existence of associations. This includes some simple formulas. Also, there is a section on webcrawlers and text mining.

Though the book is titled 'mining the www', the largest section is IR, what most would call 'search engines.' Mining itself gets only about 1/4 of the book.

The case study is fairly brief, but outlines a way to structure a simple project.

The book contains a nice bibliography.

+4 stars for such wonderful coverage of the topic. All the key attributes, major tradeoffs, and challenges are covered simply and completely.

+1 more for the *fantastic* book cover. Look at this thing, it's absolutely hilarious.

I have yet to find another text with as complete and thoroughly easy-to-understand treatment of distributed simulation as NetVE. Where is the next edition? Surely Singhal & Zyda have more wisdom to share.

This is the only book that I know of that is dedicated to networked virtual environments. This is a systems level book, so if you are looking for code listings or tons of algorithms, you will be disappointed. The author assumes that you have already obtained a basic knowledge of computer graphics, network programming, and virtual reality. This book simply integrates these sciences into the discussion of the trade-offs and decisions that must be made at a high level if you wish to design a networked virtual environment. Chapters one and two are suitable for non-technical people to read since they are discussing the promise and the origin of networked VE's. Chapter 3, "A Networking Primer", is mainly discussing networking protocols in the context of which one is best to use in particular VE situations. If you know nothing about computer networking before you read this chapter, it will leave you totally confused. Chapter 4 entitled "Communication Architecture" discusses client-server versus peer-to-peer architectures for multi-user environments and which does best in different situations. As with Chapter 3, if you do not already have an understanding of computer networking, this chapter will probably leave you confused. Chapter 5, " Managing Dynamic Shared State", discusses the issue of maintaining shared state among various hosts in a networked VE. Bandwidth, computation, latency, data consistency, and reproducibility are all discussed. To understand this chapter it would help to have had a course in operating systems on the graduate level, although, as with previous chapters, the discussion is kept at a high level. Chapter 6, "Systems Design", discusses the fundamental software architecture issues in the development of networked virtual environments. The issues covered include thread allocation for multi-threaded environments, real-time rendering and collision detection, and computational resource management. In this chapter it would help if the reader was familiar with the concepts of threads as well as computer graphics. Although the discussion is kept at a systems level, this chapter is more technical than the others, and the point of the discussion is to achieve as much of a sense of realism for the VE user as possible. Chapter 7, "Resource Management for Scalability of Performance", is even more technical than chapter six and gets down to a lower level of discussion with pseudocode being shown. The purpose of the chapter has to do with "scaling" your design so that it works equally well for small and large numbers of users. Chapter 8, "Internet Networked Virtual Environments", shows its age more than the previous chapters in that it has a pretty extensive discussion of VRML which has now become pretty much obsolete. The chapter acts as an examination of internet-based virtual environments. Also note that many of the websites shown as part of the bibliography of the chapter are now dead links due to the age of the book and the obsolescence of VRML. Chapter 9, "Perspectives and Predictions", is the final chapter of the book, and looks at the past, present, and future of networked VE's. However the "present" for this book is 1999, so this chapter has limited usefulness. This book was a five star book when it was published in 1999, and it still has many good insights, but the amount of time that has passed since its publication is starting to make it beg for a second edition. For this reason only do I subtract a single star from its rating and give it four stars. For a more recent treatment of virtual environment system design you might want to read "Designing Virtual Reality Systems : The Structured Approach" by Kim, which was published in August 2005.

This is an EXCELLENT book. Appropriate for beginners and experts in the Network VE area. Has good coverage both of technical and non-technical issues. Easy to read, informative, and presents many of the original contributions of the authors.

Provides a good grounding in issues that must be addressed by anyone embarking upon the development of massive multiplayer games. It also represents the laying of one of the first foundations of knowledge necessary for the building of cyberspace.

This is an excellent book to understand AI. Thanks to the author for his best effort. The book may be used as a text. Examples presented in the book are thought provoking. Both beginners and experts would find it useful.

This book covers a wide spectrum of tools, techniques, theory and simulation of AI and Soft Computing. The book introduces an interesting as well as impressive area i.e. cognitive modeling of human brain- definitely a new paradigm in Artificial Intelligence. Soft computing tools has been explained in a lucid manner for the under graduate and graduate students. Interesting programs on Soft Computing has been provided in the book for simulation. Particularly, the program on Genetic Algorithm for robot path planning is quite interesting. Structural organization given in the preface, helps the novice readers, as well as the teachers in covering the course contents efficiently. Another vital feature of this book is that it has been written in such a fashion that the reader can easily know the name contributors of the original research work. As a teacher of AI for the undergraduates and graduate students, I always look at the course contents of the book, coverage of each chapter and as well as the exercise there in. This book is arranged so nicely that, it becomes very easy on the part of the teacher for designing the course in a modular fashion. Secondly the exercise given in the book is of much use to the students, which I have already experienced in my tutorial class. The book shows many open problems in each chapter, which are yet to be solved. This is quite helpful to the researcher as well as to research guide. As a researcher in this area, I should appreciate the efforts put in to this book. The book can be used as a handbook of AI and Soft Computing for the students, researchers, and the teachers of this area. I strongly, recommend this book for the students of AI, which I have already done for my under graduate and graduate students. Srikanta Patnaik, Reader, Department of Electrical Engineering University College of Engineering, Burla India

This book covers a wide spectrum of tools, techniques, theory and simulation of AI and Soft Computing. The book introduces an interesting as well as impressive area i.e. cognitive modeling of human brain- definitely a new paradigm in Artificial Intelligence. Soft computing tools has been explained in a lucid manner for the under graduate and graduate students. Interesting programs on Soft Computing has been provided in the book for simulation. Particularly, the program on Genetic Algorithm for robot path planning is quite interesting. Structural organization given in the preface, helps the novice readers, as well as the teachers in covering the course contents efficiently. Another vital feature of this book is that it has been written in such a fashion that the reader can easily know the name contributors of the original research work. As a teacher of AI for the undergraduates and graduate students, I always look at the course contents of the book, coverage of each chapter and as well as the exercise there in. This book is arranged so nicely that, it becomes very easy on the part of the teacher for designing the course in a modular fashion. Secondly the exercise given in the book is of much use to the students, which I have already experienced in my tutorial class. The book shows many open problems in each chapter, which are yet to be solved. This is quite helpful to the researcher as well as to research guide. As a researcher in this area, I should appreciate the efforts put in to this book. The book can be used as a handbook of AI and Soft Computing for the students, researchers, and the teachers of this area. I strongly, recommend this book for the students of AI, which I have already done for my under graduate and graduate students.

This book covers a wide spectrum of tools, techniques, theory and simulation of AI and Soft Computing. The book introduces an interesting as well as impressive area i.e. cognitive modeling of human brain- definitely a new paradigm in Artificial Intelligence. Soft computing tools has been explained in a lucid manner for the under graduate and graduate students. Interesting programs on Soft Computing has been provided in the book for simulation. Particularly, the program on Genetic Algorithm for robot path planning is quite interesting. Structural organization given in the preface, helps the novice readers, as well as the teachers in covering the course contents efficiently. Another vital feature of this book is that it has been written in such a fashion that the reader can easily know the name contributors of the original research work. As a teacher of AI for the undergraduates and graduate students, I always look at the course contents of the book, coverage of each chapter and as well as the exercise there in. This book is arranged so nicely that, it becomes very easy on the part of the teacher for designing the course in a modular fashion. Secondly the exercise given in the book is of much use to the students, which I have already experienced in my tutorial class. The book shows many open problems in each chapter, which are yet to be solved. This is quite helpful to the researcher as well as to research guide. As a researcher in this area, I should appreciate the efforts put in to this book. The book can be used as a handbook of AI and Soft Computing for the students, researchers, and the teachers of this area. I strongly, recommend this book for the students of AI, which I have already done for my under graduate and graduate students.

The book by Amit Konar is a new addition to the disciplines of Artificial Intelligence and Soft Computing. I am greatly charmed with the presentation style of the author. The chapters on Imprecision and Uncertainty Management (ch.9), Fuzzy Petrinet (ch.10), Spatial and temporal Reasoning (ch.11), neural nets. (ch.14) and genetic algorithm (ch.15) impressed me much both for their simplicity and research-orientation. The book started with a humble beginning on psychological model of perception in chapter 2, and introduced many new concepts to formalize the theory of perception. The last two chapters in the book deal with applications of cognitive science in a criminology application and navigational planning of mobile robots respectively. The CD included with the book contains the programs used to demonstrate the concepts of the last two chapters. The programs can easily be run even by a novice user with the guidelines presented in Appendix-A.I wish you will enjoy reading this valuable book, the price paid for which is justified enough.

It is the best book on kernel methods. It covers a wide range of subjects.

The best thing is that after finishing one or two basic chapters, you can read the rest of the book in any order; most chapters are almost independent to each other. At the beginning of a chapter, the authors list the prerequistites, so a reader knows whether he will be able to understand the chapter.

For now the book still reflects the state of art. But it is a fast changing field. I hope the authors will update the book in the future.

Excellent theory on SVMs and VC dimensionality. However, I found the chapters on optimization a bit terse. Otherwise, an essential reference for those interested in using SVMs in classification and regression.

The authors are young researchers who did their Ph.D. research in this rapidly developing branch of pattern recognition. Because they are young and are at the state of the art in the filed the book has sevral advantages and disadvantages and what I see as a disadvantage someone else might view as an advantage. Anyway here is my view.
Advantage 1: Pattern recognition is a field of many disciplines. It has been studied by statisticians, mathematician, probabilists and engineering and people that call themselves computer scientists specializing in artificial intelligence. The field is old and has a long history but each discipline has developed their own jargon and many times the wheel has been reinvented. The advantage of this book is that these young scientists don't see that awful history. They have learned and mastered their subject in a basically engineering jargon but they include many concepts from statistics and statistical learning theory that are not common to engineering texts. This includes such topics as robust regression, ridge regression and spline estimation. Much of the classical statistical literature is cited. The book contains over 600 references including much of the authors own work.
Disadvantage 1: Because they are young they miss some of the important historical literature and key texts. I found it a little disappointing that the bootstrap which is a statistical tool that has played a major role in discriminant analysis (particularly in the estimation of classification error rates) was completely overlooked. Also although many important texts on pattern recognition, machine learning and discriminant analysis are cited the fine text by McLachlan is overlooked as is the recent relevant text by Hastie, Tibshirani and Friedman.

Advantage 2: This book highlights the work of Vapnik and Chervonenkis and provides nice concise descriptions that one can easily refer to when needed. The mathematics is deep and includes reproducing kernel Hilbert space and many important properties from functional analysis and statistical theory.

Disadvantage 2: The authors are more experienced at writing professional papers than at writing text books. Consequently the book does not flow well and the authors freely admit in their preface that it is best not to read the book in sequential order but rather to take the suggestions in the preface that differ based on the readers background and interest.

Having said all this, for someone like me, who is very knowledgeable about statistical pattern recognition this is a great text for getting me up to speed on an exciting new area that I know very little about. I became curious about it when I started reading Vapnik recently.

I am hoping that a careful reading of this book will give me an intuition about why this approach that incorporates kernel methods can be a powerful tool in pattern recognition and classification.

This book should be a useful reference for anyone interested in this research area. It could be used in an engineering or statistics course in pattern recognition at either the undergraduate or graduate levels depending on what material is covered.

In a recent communication with Bernhard Scholkopf I learned that his book was sent for publication before the Hastie et al. book went to press. So that is the only reason it wasn't referenced. I think that point is worth my mentioning in an editing of this review. Also on reflection I do not think the disadvantages are so great as to remove a star. So it is 5 stars for them.

I can only hope that they will reference the work of McLachlan and Hastie et al. in their future books and research on this subject.

This book is at the right level if you are already strong in Machine Learning theory. (e.g. Tom Mitchell's "Machine Learning").

Note that it is already getting somewhat dated. It for example includes little information on kernels for discreate structured input, such as trees and graphs.

Great book, but a word of caution, it is not for the novice.
Book assumes a lot of background in functional analysis and
probability. True, it has extensive appendixes but they are
short-handing the relevant materials only. However, having said
that, this is a book worth struggling with even if you have not
yet got the intuitions in the above mentioned disciplines.

It is worthwhile (at least as I can tell) to read the book
skipping the tool chapters (2-6) going back to them when one has
a point where those are needed. I found that to be much easier
as it provides a concrete use of the methods putting them
in context.

Book is excellent. Covers the theory very well, such that you can make the computer code yourself. They also provide puedocode. You will be able to learn it better than other books that just give you the code. I find that once you understand the theory, writing the code is easy.

Everybody who tries to use NNets for real goes through these steps.
First, there is the Delta rule.
Then, there is overfitting, local minima, generalization problems and frustration.

The complexity of NN is not in it's math; the difficulty is in the construction of a NN. This book is excellent in providing rules-of-thumb for NN construction, while at the same time providing the theoretical backing.

Hey I am not making money reviewing this book, it's just really good.

Many textbooks can help me to understand the different concepts of neural network, but not the practical tips needed to optimize neural network anlysis and implementation.

The topics covered are reminicent to those discussed in part 2 and 3 of the Neural Network FAQ. In chapter 6, the relationships between learning rate, momontum, trainig time and learning modes are presented graphically. With this, it helps me to rule out and avoid learning parameters that are unlikely to improve the NN performance. This is especially important if the dataset is large and the NN program is implemented in Java.

If the aim is to develop a NN solution that will give you the best results, I find both chapter 7 (heuristics for weights initialization) and 16 (heuristics for improving generation) are esential and saves me a lot of time from reading many journals.

In summary, this book has helped me to develop the art of NN optimization. It shows me how to visualize decision surface and the various graphical relationships between learning paramters and various components of NN topology. I think you will find this book very useful after your NN program is up and running and you are looking for ideas and explaination on how to improve the NN performance further.

After owning this book for many years and reaching for it many times, I decided that I at least owed the book a five star review.

Early in my graduate career I began working with neural networks and discovered this book in a electronic bookshelf available at my university. After printing chapter after chapter to read on subway rides home I ended up buying it for convenience. It gave me the background I needed to code up a basic artificial neural network in C++ and to then extend it to fit my needs.

The style of the writing is the perfect balance of enough detail to understand a concept or method without unnecessary wordiness. Each chapter covers an important aspect of neural network development and application - for exmaple, internode weight initilaization techniques - and acts a sort of mini-review of the most popular methods with a clear explanation of the pros and cons of each.

This is an excellent bookshelf addition for anyone who works with neural networks.

Some books just have the right feel about them and this is one of them. The author clearly knows neural networks and also knows how to communicate to others in a no-nonsense fashion. With so many books being published, you rarely find a technical book that is as good as this. The emphasis here is on conveying the insights that the experts in this area would know. Importantly, concepts are explained equally in words, graphics and mathematics, maximising the uptake of knowledge from the book. Tufte would be impressed by the quality of the line graphics in this book, and the information that they convey, not to mention the overall presentation. I suspect that this book would make an ideal textbook for a course in neural networks. Overall, I've enjoyed reading it very much.

The strength of this book is its combination of breadth and completeness: there is working code (well beyond the toy stage) of a large number of different AI systems that cover a large subset of what is commonly considered AI.

The programming itself is rather basic, and very straightforward. In many places an advanced programmer would have avoided a global variable, unified code through the use of higher-order functions, had functions communicate through a shared local environment, created a lazy list, you name it.

The author avoids most of these more advanced approaches in order to present the ideas behind the approaches without being sidetracked into programming technique issues, and that is the correct choice for this book. Even as it is, there is already the duplicity of teaching Common Lisp and teaching AI programming.

That being said, the code in general is not bad at all, even though I wouldn't want my students to learn CL programming from it. The author has simply bent down to the level of, a good C programmer, and worked from there. His main intention being to teach AI programming approaches, he has spent much less time to raise the programming level of his audience.

Knowing the author's level of Lisp programming, I can't wait to see a book by his hand on how to use abstraction as an organising principle in programming.

I have no background in computer science or AI, but found myself needing to use Lisp for various creative and artistic purposes. I've spent a lot of money on books relating to Common Lisp, but I wish I had just gotten this one and Touretzky's "Gentle Introduction to Symbolic Computation." The particular strengths of this book are its detailed discussion of advanced topics, especially optimization, and the practical overview of current and historical AI topics through programming examples. Very clearly written.

This book is equally excellent regardless of whether you wish to regard it as:

a) A historical study of Artificial Intelligence, with USABLE examples of code, or

b) A book presenting techniques for programming in Common Lisp.

As a reference about Common Lisp, it is certainly lacking, but this is no great problem when both the Common Lisp HyperSpec and Steele's book are readily available in electronic form. It provides something more important: SIGNIFICANT examples, and significant discussions on WHY you would use various Lisp idioms, and, fairly often, discussions on HOW pieces of Common Lisp are likely to be implemented. Its discussion of an implementation of the LOOP macro, for instance, provides a very different point of view than the "references" to LOOP. (Contrast too with Graham's books, which largely deprecate the use of LOOP.)

From an AI perspective, it is also very good, providing WORKING SAMPLES for a whole lot of the historically significant AI problems, including Search, PLANNER, symbolic computation, and the likes.

It would be interesting to see parallel works from the following sorts of perspectives:

- The same sorts of AI problems solved using functional languages (e.g. - ML, Haskell), to allow contrasting the use of those more modern languages. Being more "purely functional" has merits; such languages commonly lack macros, which is something of a disadvantage.

- The use of CL to grapple with some other sorts of applications, notably random access to data [e.g. - databases] and rendition of output in HTML/SGML/XML [e.g. - web server].

This book has been called "The best book on programming ever written". I'd have to agree--it is certainly the best that I've ever read.

William Zinsser said, "The essence of writing is rewriting" and the same can be said for writing computer programs. Norvig's book presents this process--how the limitations of a program are overcome by revision and rewriting. What sets Norvig apart as a writer is that, amazingly enough, he can write about debugging (the most dreaded part of computer programming) and make it a fascinating read!

Lisp has been getting a higher profile lately because of essayists like Paul Graham and Philip Greenspun; in particular, Greenspun's Tenth Rule of Programming which states: "Any sufficiently complicated C or Fortran program contains an ad hoc, informally-specified, bug-ridden, slow implementation of half of Common Lisp." So, should this book be read as an exhortation to return to Lisp as the preferred programming language?

Paradoxically, I think not. One third of the way through the book, Norvig shows us how to implement Prolog in Lisp. From then on out, most of the AI techniques he presents either directly use Prolog instead of Lisp (such as his excellent discussion of natural language processing using Prolog) or use Prolog as a base to build on (such as his discussions on knowledge representation).

From this we can abstract what I'd like to call Norvig's Corollary to Greenspun's Tenth Law of Programming: "Any sufficiently complicated LISP program is going to contain a slow implementation of half of Prolog". I'm leaving out the "ad hoc", "bug-ridden" part of Greenspuns's law, because Norvig's programs are neither. But it is quite remarkable the degree to which, once having absorbed Prolog, Norvig uses Prolog as the basis for further development, rather than Lisp.

Is this a book about Prolog then? Again, no. What is the take-away message? It is this: as our world becomes more and more complex, and as the problems which programmers are facing become more and more complex, we have to program at a higher and higher level.

Norvig does not stop at just embedding Prolog in Lisp. He also shows us how to embed scheme as well. Excellent discussion on the mysterious call/cc function and on continuations.

In a capsule review, it is impossible to really give an overview of a 1,000 page book like this one. But the scope and heft of the volume really needs to be commented on: the programs presented in this book are like basis vectors, the totality of which nearly span the space of programming itself. In no way should this be considered "just an AI book" or "just a LISP book". This book transcends language, time, and subject matter. It is a programmer's book for the ages.

"Paradigms of Artificial Intelligence Programming" is one of the best books of computer science that I have ever read. I put it up there in the pantheon with "Structure and Interpretation of Computer Programs". I have found more useful and mind expanding material in these case studies than I have in many other books on computer science. I highly recommend this book to anyone, even if they have never used Lisp.

The computer and natural language is a sub-domain of computer science in which one of the major aims is to imitation of man, focusing on two topics: psychology and linguistics. If we wish the machine to do something, we must tell it what to do and it must be able to understand us. The easiest way to tell a computer what to do is to give it a program to run. "Humans, if they are machines at all, are vastly general-purpose machines and what, is most important, they understand communications couched in natural language." Work must be done for a machine to understand natural language. "Man's capacity to manipulate symbols, his very ability to think, is inextricably interwoven with his linguistic abilities." A machine must be able to extract semantic content from the messages impinged upon it, adopt a syntactic structure of a visual scene and adopt a certain conceptual framework. The question of what comprises a visual symbol is in question. The developer defines the elements of the machines primitive vocabulary. Robert Lindsay said, "high quality translations could be produced by machines supplied with sufficiently detailed syntactic rules, a large dictionary, and sufficient speed to examine the context of ambiguous words for a few word in each direction."

Eliza was a program consisting mainly of general methods for analyzing sentences and sentence fragments, locating so-called keywords in texts, assembling sentences from fragments and so on. Eliza created the remarkable illusion of having understood in the minds of the many people who conversed with it.

In ordinary two person communication, each has a working hypothesis, a conceptual framework, concerning who the person is and what the conversation is about. The hypothesis serves an indicator of what the other person is going to say and what he is going to mean by what he is about to say. Often, the erroneous prediction is falsified before the sentence is completed and the listener makes corrections on the fly and virtually unconsciously. Each brings into mind an image of the other person, the image consists in part of the other's identity, attributes based on evidence derived from independent life experiences of the participant. "Our recognition of another person is thus an act of induction on evidence presented to us partly by him and partly by our reconstruction of the rest of the world; it is a kind of generalization". Eliza starts with the hypothesis that the system does understand.

Rogar C. Shank, based his theory on the central idea that every natural-language utterances is a manifestation, an encoding, of an underlying conceptual structure. Understanding an utterance means encoding it. The theory proposes a formal structure for the conceptual bases for making predictions. The theory creates formal rules for converting utterances into a conceptual base. One difficulty is that every individual's belief is constantly changing mean that an individuals entire base of conceptions is changing. "When a person enters a conversation he bring his belief structure with him as a kind of agenda."

Terry Winograd, of M.I.T, was working with a group were building a computer-controlled "hand-eye" machine; the computer could see its environment and manipulate objects in its environment by means of a computer-controlled mechanical arm. Winograd design and coded the software to enable humans by natural language, too instruct the computer, how to manipulate and explain events with respect to the toy world of blocks, in a natural language. "The robot can manipulate toy blocks on a table containing simple objects like a box." The robot could be ask to manipulate the objects, doing such things as building stacks and putting things in a box. It could be questions about the configuration of blocks on the table, about events that were going during the discussion, and it could be told simple facts about the objects which could be stored and used for reasoning later. The conversation goes on within a dynamic framework - "one in which the computer is an active participant, doing things to change his toy world, and discussing them."

An authority in the field of artificial intelligence and computer science in general, Joseph Weizenbaum provides insight in proceedings in that area but mainly warns about what these developments may lead to. It is very entertaining to read this book some 20 years after original publication and see how many of what we believe are recent developments were actually implemented back then already (on one or two priceless "super" computers).
Very dogmatic and patronizing at times, it still is a good read if only for the thought provoking ideas like: if electronic computers would have been used in the manhattan project, today we would assume that development of the atomic bomb would have been impossible without it.

This book is a basic philosophical treatment of computing. I think that it should be included as a basic part of any Computer Science / Computer Engineer curriculum in respectable universities, along with Roger Penrose book, The Emperor's new mind, it creats a better understanding of what is human and what is mechanic for all those who need to know it.

Should Computer Science / Engineering freshmen/women in universities know? My answer is YES, in their first year !

I read parts of this book, thinking highly of it. I thought one particular passage from it, as quoted in Gates by Stepehen Manes and Paul Andrews, particulary stood amid the limelight: [t]he computer programmer . . . is a creator of universes for which alone is the lawgiver. . . .No playwright, no stage director, no emperor, however powerful, has ever exercised such absolute authority to arrange a stage of field a battle and to command such unswervingly dutiful actors or troops.

This is perhaps the best book ever written on issues of computer technology and modern life, in the sense that it says a lot of really important things and is also very readable by both lay persons and technical persons. People like Jacques Ellul, Arnold Gehlen et al. have written very important texts in this area, but are much less "accessible". If the truth only counts when it is absorbed by persons, Weizenbaum's book stands out as being engrossing and a pleasure to read, as well as saying what needs to be said. It is very sad that the second edition which was supposed to be out a year or so ago has not appeared. But in no way has 20 years "dated" the present text. _Computer Power and Human Understanding_ explains why we have such problems as Y2K, etc.