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Customer Reviews (127)

Is there more to it?
A very well presented book on the human mind and one of the leading theories concerning intelligent thought.My only caveat is this: It feels like the author wants to find the simplest solution possible for intelligence - that human beings are pattern recognition predictive organisms.I challenge the idea that what I call "eureka moments," or intuitive leaps, are the simple result of pattern recognition.I simply feel that there must be more to intelligence.

Despite its complexity, the brain's true beauty & ingeniousity is in its simplicity.
Jeff Hawkins proposes (or, maybe, bundles in a neat package) a very interesting framework of thinking about intelligence.
Made me realize that, if you have the vision to see beyond its complexity, the true beauty and ingeniousity of the brain (and of intelligence, as a whole) is its simplicity.
Highly recommended.

Mostly about the neocortex's mechanisms
This book is mostly about theories on how the human brain's neocortex basically functions. The ideas presented are very plausible. It gives you some general ideas which you probably could incorporate into your own AI algorithms. The book primarily discusses how the brain processes input from the senses. However, this book does little to explain how the brain understands things and it does little or nothing to explain how the brain solves problems. I'm not really interested in creating AI programs that process sensory data such as vision and sound. I want to create general intelligence software that can understand things and do original thinking.

After reading the book, it becomes clear that the brain is extremely different from computers. The neocortex has billions of neurons, each of which has thousands of synapses. I'm pretty sure it would be impossible to replicate that in silicon chips. Also if we tried to replicate it with software, it would probably be much too slow. So therefore, mimicking the brain's cell structure would be like trying to build an airplane with flapping wings or a car with legs. The brain's structure is optimized for use with living cells, not silicon or software. Therefore studying the structure of the brain is of limited benefit. You have to take from the brain what you can apply to machines and discard the rest.

This book only goes a small way towards creating general intelligence in machines, but it does present some good concepts which probably can be transferred to software algorithms.

The crux of intelligence!
The ability to make predictions about the future is the crux of intelligence!
And Jeff Hawkins book ''On Intelligence'' presents some brilliant ideas on how the brain might actually be doing this.

Sure, some might say that because the brain is so complicated, we will never really understand how it works. But according to Hawkins, complexity is a symptom of confusion.
Indeed, we need some good core ideas that can help us make sense of the whole thing. In Hawkins book, the core idea is seeing the brain as a memory-prediction system. A memory system, that store experiences in a way that reflects the true structure of the world. A system that remembers sequences of events and makes predictions based on these memories. According to Hawkins, such a system is the basis of human intelligence, perception, creativity, thoughts and even consciousness.

The brain doesn't ''compute'' answers to problems. It retrieves the answers from memory. And that is why the brain can be so fast, even though neurons really aren't that fast.
It only takes a few steps to retrieve something from memory. Slow neurons are not only fast enough to do this, but they constitute the memory themselves. The entire cortex is a memory system. It isn't a computer at all.

Hawkins book is a real page-turner. Exciting and fascinating throughout.
A brilliant book that gives some really good insights into how the
brain might actually work.

-Simon

A Common Cortical Algorithm
In "On Intelligence," Jeff Hawkins presents a new theory about how the brain works and how we can finally build "intelligent" machines.The neocortex, the center of higher thought, is the focus of attention here.Hawkins says that neuroscientists are lost in the complexity of mapping out neural pathways, and are not coming up with compelling overarching theories that begin to explain how we think and learn.

He believes there is enough evidence now to posit a common cortical algorithm, as first proposed by Vernon Mountcastle, a neuroscientist at Johns Hopkins, in 1978. The algorithm is hierarchical, with lower layers encoding data from a sensory organ, but higher layers dealing with abstract signals that bear little resemblance to the sensory signals.Hawkins asserts that brain researchers got sidetracked partly due to the experimental difficulty of taking measurements.The standard approach is to present a static sensory stimulus and take readings of resulting cortex activity.It is too difficult to work with dynamically changing stimuli, so researchers have missed a point that Hawkins believes is crucial: the brain can only perceive dynamic stimuli.

Hawkins' theory, called "Memory Prediction Framework," defines intelligence as "the capacity of the brain to predict the future by analogy to the past."According to him, there are four key attributes of neocortical memory that differ from computer memory:
* All memories are inherently sequential.
* Memory is auto-associative; a partial memory can be used to retrieve the full memory.
* Memories are stored in invariant representations.
* Patterns are stored in a hierarchy.
Support for the theory is most concretely expressed in chapter six, the meatiest part of the book. This is where the author describes in some detail his vision of how the neural circuitry in the layers of cortex works.The description is compelling, but takes more work to follow than the other chapters.

Chapter six ends with several fascinating observations that are built on top of the neural circuitry described earlier.It emphasizes that perception and behaviour are highly interdependent because they both originate in a detail-invariant representation that is then transmitted through both motor and sensory cortex.Also, although many researchers have discounted it, Hawkins argues that feedback and the importance of distant synapses in cortex is essential to explain the Memory Prediction Framework theory, and should be reconsidered.The theory includes the broad principles of how hierarchical learning of sequences explains how children first learn letters, then words, phrases and finally sentences, and as adults we can speed-read without needing to study every letter.The author believes that the memory of sequences re-forms lower and lower in cortex, allowing higher layers to learn more complex patterns.Finally, the hippocampus is briefly described as logically residing at the top of the cortical hierarchy: the short-term repository of new memories.

An impressive result of the speculations in chapter six is a list in the appendix of 11 specific, testable predictions made by the theory, which is an invitation to brain researchers.And Hawkins founded a company, Numenta, to develop the Hierarchical Temporal Memory concept based on the theory.
Chapter six also hints at how daydreaming or imagining occurs, when predictions from layer 6 of a cortical column are fed back to layer 4 of the same column.Cortical modeller Stephan Grossberg calls this "folded feedback".In chapter seven the book expands on philosophical speculation about the origin of consciousness and creativity that arise from the Memory Prediction Framework theory.Creativity is defined here as "making predictions by analogy". As the author says, there is a continuum of creativity, from mundane extrapolations from learned sequences in sensory cortex to rare acts of genius.But they have a common origin.This is how a piano player can quickly figure out how to play simple melodies on a vibraphone, or a customer in a strange restaurant can figure out that there is probably a restroom in the back.Creativity is so pervasive that we hardly label it as such, unless it violates our predictions like an unusual work of art.There are practical suggestions in this section for how to train oneself to be more creative, and an interesting story of how Hawkins conceived the handwriting recognition system, Graffiti.

Chapter seven ends in speculation about the nature of consciousness, imagination and reality in response to the inevitable questions to which this type of work gives rise.A review on the Amazon website by Dr. Jonathan Dolhenty takes issue with what he describes as "plain old-fashioned metaphysical materialism and, probably, old-school psychological behaviourism," which are largely discounted theories today.Dolhentyis a philosopher who thinks human intellect at the higher abstract and conceptual levels cannot be described by such a simple extrapolation of the Memory Prediction Framework.But I found the connections made between brain theory and "mind" reassuring.Leave it to others to build on this foundation.In fact, Hawkins does hint at a broader source of the mind in chapter seven, where he says that it is influenced by the emotional systems of the old brain and by the complexity of the human body.

The last chapter in the book contains another vision, of how intelligent machines might be built in the future.This is back into the Popular Science mode.Unlike many current roboticists who believe humanoid robots will be needed to interact with humans, Hawkins believes humanoid form is pointless and impractical.He advocates working from inside out, by building sensing mechanisms and attaching them to a hierarchical memory system that works on cortex principles.Then by training the system he believes it will develop its own representations of the world.This system can be built into any sort of machine, and the sensors can be distributed if desirable.

The technical challenges of building an intelligent machine include capacity, which by analogy to the brain, at 2 bits per synapse, would require 8 trillion bytes of memory or about 80 hard drives.Connectivity is a larger problem, since it would be impossible to provide dedicated connections.Hawkins believes the answer would be some sort of shared connections, like in today's phone network, but this is still a challenge.

As an aside, there is no mention of the Cyc project, which has been working since 1984 to build a mammoth semantic knowledge base.But unlike the automatically learned representations in Hawkins' proposed artificial brain, the ones in Cyc are hand-input in a preconceived structure as a vast quantity of terms related by assertions.

The last chapter ends with a very positive view of the potential of intelligent machines to solve problems humans cannot, because they can be equipped with custom senses, immense memory, and even be networked to form hierarchies of intelligent machines.Hawkins believes that intelligent machines will be a hot topic in the next ten years.It is easy to get caught up in his excitement.
... Read more

Product Description

Artificial Intelligence is one of the most rapidly evolving subjects within the computing/engineering curriculum, with an emphasis on creating practical applications from hybrid techniques. Despite this, the traditional textbooks continue to expect mathematical and programming expertise beyond the scope of current undergraduates and focus on areas not relevant to many of today's courses. Negnevitsky shows students how to build intelligent systems drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation and now also intelligent agents. The principles behind these techniques are explained without resorting to complex mathematics, showing how the various techniques are implemented, when they are useful and when they are not. No particular programming language is assumed and the book does not tie itself to any of the software tools available. However, available tools and their uses will be described and program examples will be given in Java. The lack of assumed prior knowledge makes this book ideal for any introductory courses in artificial intelligence or intelligent systems design, while the contempory coverage means more advanced students will benefit by discovering the latest state-of-the-art techniques.

Customer Reviews (5)

Undergraduate Textbook
I got this book as part of a short course in AI by Negnevitsky that I attended a while back. The course was, in my opinion, too short for the material covered. The book, however, appeared to be more promising. I'll start with the good points. First, it is well-written and covers the "essentials" of AI such as expert systems, fuzzy logic, neural networks, genetic algorithms, hybrid intelligent systems and data mining. Second, each chapter is well-organized with sufficient examples, a summary of key points and questions for review at the end. Third, at just over 400 pages and being only around 9.5 x 6.25 inches, it is also quite easy to carry around and read at your convenience. Fourth, the pages are bright white with crisp black text which also makes for easy reading even where lighting is not perfect.

However, I do have a few issues with the book. First, it does not really cover things like Monte-Carlo search, the minimax algorithm (used in chess) or swarm intelligence, to name a few. I found that as I looked for clarifications about certain things, I came across these other topics which weren't in the book; which brings me to the second issue. The beginning of each chapter is seductive with its easy-going introduction and general overview, especially to the uninitiated, I would imagine. However, the average reader (I have advanced degrees in computer science, by the way) will likely find himself trying to catch his breath after that. There is a little too much content squeezed into too few pages. Even more, Negnevitsky uses a considerable amount of mathematics, charts and diagrams which are not always easy understand. It is assumed, of course, that the reader has a "basic" understanding of math. If "advanced" math is used in say, rocket science, "basic" is just a relative term. If you simply skip over these things or assume they are true without trying hard to really understand them, you will not likely learn as much.

I did not intend to read this book to relive my undergraduate course in AI but it put me through it nonetheless. I was actually hoping for a less technical but sufficiently lucid explication of the different approaches currently used in AI; a "refresher" course, so to speak. Something that would explain the general principles without focusing too much on actual pen and paper calculations (which are unnecessary, even if one works in AI, unless one actually plans to employ a particular approach; in which case they can pursue it further elsewhere). In that respect, I was somewhat disappointed. This book appears to be intended mainly for undergraduates with the "be ready for the exam" mentality.

The problem is, by the end of the book, you begin to wonder just how much you've really learned. I would say it unlikely reaches even 50% of all that has been jam-packed into this book. To test this hypothesis, just see how many of the "questions for review", in total, that you can answer correctly after reading the whole book. Not to mention actually being able to do the kind of calculations the book seems to emphasize. To summarize the second issue, the book kind of pulls the reader away from gaining an important conceptual perspective of AI techniques and how they relate to each other. This is still possible despite the undergraduate and generally technical nature of the book but you will have to be careful to see the forest for the trees. Having both a strong, technical grasp of the techniques *and* a conceptual overview of how they relate to each other as a field is what, I think, the book tries to do but falls short at the expense of one.

The third issue pertains to the *ten* case studies at the end of the book. I'm not really sure that many are necessary, though (something to keep in mind for a possible 3rd edition of the book). While some of them are a refreshingly straightforward read, by the end of the book, you will likely find yourself having to go back to the chapters in which the techniques employed were initially explained to really make sense of them (even more so if you had skipped over the technical parts, which I didn't). In certain cases, Negnevitsky seems to have forgotten that while this book was "developed from lectures to undergraduates" (see the back cover), his readers are not necessarily attending those lectures afterward to ask for clarifications. For instance, in Case Study 9, he mentions the Gini coefficient and says they were used in Figure 9.46a but it is not explained *how* exactly they were used. If you look up the Gini coefficient in Wikipedia, it doesn't help much in this context, either. I, for one, was not previously familiar with it. The fourth issue is that I think there is also at least one significant error in the book in Figure 9.22. It says on page 327 that we can improve digit recognition by feeding the network with 'noisy' examples and that this is shown in Figure 9.22 (on the next page). However, the figure seems to show that the network trained with noisy examples has a higher percentage of recognition error. How is this an improvement?

Another thing I noticed is that there isn't really an equal treatment of even the topics covered. Fuzzy logic and neural networks seem to come up more often. This can be condoned to an extent but I really did not see the purpose of bringing up Adaptive Neuro-Fuzzy Inference Systems (ANFIS) as part of an "introductory text for a course in AI" and later referencing it in Case Study 8, which implies that it should be properly understood. Perhaps it deserved better treatment in the context of this book. Genetic algorithms, on the other hand, was nicely explained and later made Case Study 7 relatively easy to understand. Finally, I have to say that the cover art does the book only further injustice.

In summary, I would still recommend purchasing this book because some parts are beautifully explained and this is good for quick reference, especially when memory fails. However, there is still room out there for a less-technical, conceptually-inclined *introduction* to how things work in AI. Such a book may not be on the required reading list of undergraduate courses in AI or advanced courses in philosophy but it would probably be much more accessible to the public and even computer scientists in general.

explains key ideas with minimal maths complications
The field of Artificial Intelligence has been around for decades. During which there have been numerous advances and disappointments. Often, the advances have been described in other texts using highly mathematical treatments. All to the good. Except that this does tend to act as a barrier to newcomers to AI, who might not have a very strong maths background. And even for those who do, the sheer amount of maths to understand in those books can be time consuming.

Which is the attraction of Negnevitsky's approach. He deliberately de-emphasises the maths. Enough is retained to give a valid treatment. But it is now far easier to understand the underlying ideas. Such as artificial neural networks. Here, I was also impressed to see him give proper prominence to John Hopfield's seminal contributions to neural network theory.

More generally, the book covers well the entire breadth of AI. From fuzzy systems to genetic algorithms to rule-based systems.

A very good introductory text book for intelligent systems
The author explains various AI concepts in very simple terms and has managed to present the math behind some of the ideas in an understandable manner.

The treatment of various topics is intermediate though but it is a good place to start and does not leave the reader riddled with complex math equations.

In-fact the author has done a great job at keeping the concepts separate from the mathematics, except for some places like neural networks where it is not possible to explain the concepts without talking about the math involved.

Instead of focusing too much on a particular aspect of intelligent systems this book deals with a whole spectrum of technologies such as fuzzy systems, neural networks, hybrid systems etc.

The writing style of the author is very simple and clear and it is possible to finish the entire book over a period of one semester or a little more.

Excellent Treatment of Complex Topics
What Dr. Negnevitsky states in the preface of this book, "Most of the literature on AI is expressed in the jargon of computer science, and crowded with complex matrix algebra and differential equations" is an accurate assessment of current textbooks that try to go beyond just the basics of AI.

Actually, this book does contain some of the same complex material that Dr. Negnevitsky accuses others for having with one exception:He does a terrific job in simplifying the complex theories behind them.

At first, when I flipped through the pages, huge equations and matrices jumped at me.My first impression was that this book was for serious computer scientists or mathematicians.I was looking for simpler material for my beginning AI students.I started reading the preface and found the argument interesting.

I speed-read through the first chapter and found the history of the field presented in a concise and a very well laid out fashion.I jumped into reading the beginning of chapter 2 and I was amazed at how well Dr. Negnevitsky progressed from basic ideas to more and more complex layers.With other similar books, the reader will need many basic theory books (mathematics, basic AI...) in order to understand the topics.Dr. Negnevitsky provides all the basics necessary.This same strategy is repeated for the remaining chapters.

I acquired the book and read it from beginning to end.I found the material consistently well presented.One warning: this book does get very technical and complex in many chapters.However, the material in each of those chapters is progressively laid out.Even if a reader stops in the middle of some chapters, there is still a lot to gain from the experience of reading the entire book.I highly recommend it to anyone interested in really understanding beyond just keywords and delve into the internals of AI topics.

Thanks to Dr. Negnevitsky for a great book.

Great Introductory Book on Soft Computing
For a beginner that wants to know where the stories about Soft Computing really converge, this book is a starting point. The style of the author is simple and great.

My interest was to get a book that keeps the daunting mathematical jargons in Fuzzy Logic (contained in several other books) minimal, while presenting the concepts. I fell in love with this book, that I had to run through all the pages as if it's a novel.

This book really demonstrates that the whole idea behind intelligent systems are simple and straightforward. You do not need another teacher. He presented algorithms (e.g. back-propagation)in a very simple to understand manner.

Dr. Michael Negnevitsky, the author, must be a great teacher. It's a handy and nice book. I strongly recommend it. ... Read more

Product Description

Intelligent agents are employed as the central characters in this new introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. The book provides a refreshing and motivating new synthesis of the field by one of AI's master expositors and leading researchers. Artificial Intelligence: A New Synthesis takes the reader on a complete tour of this intriguing new world of AI.

* An evolutionary approach provides a unifying theme
* Thorough coverage of important AI ideas, old and new
* Frequent use of examples and illustrative diagrams
* Extensive coverage of machine learning methods throughout the text
* Citations to over 500 references
* Comprehensive index ... Read more

Customer Reviews (15)

Good general overview
The field of artificial intelligence has an interesting history, both in terms of its content and the philosophical debate it has provoked. The field could also be loosely described as divided into two camps, those who view it as a collection of highly sophisticated algorithms, and those who view it as an attempt to create machines that exhibit human-level intelligence. Ironically, in the latter camp, it is difficult to assess the progress that has been made, since criteria for measuring machine intelligence are never explicitly given. Instead, dependence has been made on the "Turing test" for intelligence, a test that is difficult to apply, and in fact can be said to be too vague for a practical, objective assessment of machine intelligence.

This book is written more in the context of the latter camp, than in the former. However, in-depth discussion of the Turing test is not given, and this actually is one of the main virtues of the book, although the author clearly believes that the purpose of doing research in artificial intelligence is to achieve human-level intelligence. As he remarks in the last paragraph in the book, it was written to overview the techniques that he believes are required to achieve human-level intelligence. Although he does not explicitly give the reader tests for machine intelligence that will allow progress to be measured, he devotes a small portion of the book to various ideas on just what constitutes intelligence.

The book also gives a general (and sometimes very brief) overview of the algorithms used in artificial intelligence.Search heuristics, neural networks, and genetic programming are some of the topics that are covered. The influence of the "intelligent agent" paradigm, that is now taking the AI community by storm, is very apparent throughout the book. The author though does not neglect some of the topics in "good-ole-fashioned" artificial intelligence that arose decades ago and is still applicable today, especially in the field of logic programming. These topics include resolution in both the propositional and predicate calculus, and in expert systems. By far the best discussion in the book is on knowledge-based systems and evolving knowledge bases. This topic has taken on considerable importance in recent years due to the importance of data mining and business intelligence.

Readers who are considering artificial intelligence as a career choice will find good motivation by reading this book. The field also is quite different than most others in that it respects a high degree of individual creativity and ingenuity, and has a high bandwidth for new ideas. Beginning with its origins in the 1950s, the field has grown by leaps and bounds, but its applications have exploded in the last five years, fueled mainly by business and financial applications. Concerned not only with achieving human-level capabilities, but also with other forms of intelligence and how they can be useful, artificial intelligence has become one of the predominant forces in the twenty-first century. One can only be excited and optimistic about its further advances.

Run Forrest Run
In general avoid this book.
I purchased this book for a course, and unfortunately this is my first book. Its 95% maths, of course AI is a lot of math, but the book is so abstract and nothing related to practical stuff. Take convolution filters, it gives integrals and all that stuff, but what exactly does it do, how does it perform it on images, and where the heck are sample images, and sample matricies.
I bet this author must have sent this book out to teachers so that 50 students would have to buy this over priced book with no practicle use and so hard to read/understand and extremely dense.

Not a good intro to AI
While the book is well organised and number of topics covered is substantial, this was the worst intro-to-anything book I had to suffer through. If calculus is something you are very comfortable with, then go ahead, read it. :-)

nice, but with these errors
A nice book. Especially the order in which the topics are covered is a good idea. However, you will not find the following errors reported in the book's webpage:

Page 52: The "high-degree function" is not a function!

Page 92: In Figure 6.6, the topmost pixels that get deleted as a result of the averaging operation should actually remain there, since both their sums are 4, which is greater than the threshold, which is 3.

Page 100: In Fig. 6.13, the last row of the last image contains a spurious image boundary.

Page 151: In Fig. 9.8, there are two nodes with name n; the one which is higher in the figure should have the subscript 1.

Page 152, item 3 in the list: There is an implicit assumption that h-hat always returns 0 for goal states. I don't think that this assumption is stated earlier in the text.

Page 165: In Figure 10.1, all arrows are supposed to be pointing away from the current state.

Page 246: The last paragraph mentions ".. the two interpretations for Clear and On suggested by Fig. 15.2", but aren't actually THREE interpretations suggested for On?

And in the current errata list in the book's website, something is clearly wrong with item 6, since it says n_i should be replaced by n_i.

All in all, a good book.

Varies between being superficial and incomprehendable
After having borrowed and read part of Nilsson's previous book "Principles of Artificial Intelligence" at the library some years back I was quite positive about the prospect of reading this one. However, it falls short on many of my expectations and can therefore not be recommended for neither the beginner nor the expert.

The book covers all the major areas of artificial intelligence but does so in a very superficial manner. There isn't actually enough information in the book at allow to to implement some of the techniques available - it is mostly teasers. Also many of the subjects are - and even some of the subjects that I already knew about beforehand - incomprehendable and I often got more confused about a subject than before I began reading it.

I very rarely give a book one star, but this one deserves it in the light of the many better books on AI. I recommend that you read "Russell and Norvig: Artificial Intelligence - A Modern Approach" instead.

Jacob Marner, M.Sc. ... Read more

Product Description
As the power of Bayesian techniques have become more fully realized, the field of artificial intelligence (AI) has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition, but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' Web site. ... Read more

Customer Reviews (3)

Very good introduction in causal Modeling
The book by Korb and Nicholson is very readable and structured. Starting with some background information in statistics it comes directly to the major topic of the book - bayesian networks. The theory thereof is nicely evolved and applied to small examples to demonstrate its usage. Each chapter finishes with a short summary and bibliographical notes for further readings.

In my opinion this book is well written and the chosen examples are insightful. What I do not like is part three of the book which is devoted to case studies and praktical examples. If this space had been used for the first two parts by providing more details, e.g., for the discussion of path models (which is given but only short), this book could be even great on a more advanced level. In this form it is very good as an introduction in Bayesian Networks and related topics like the larger class of causal models.

Excellent Introductory Text
It is difficult to assess a review without understanding the biases of the reviewer.I fall under the category of researcher/practitioner when it comes to reasoning with graphical models.I am familiar with and make use of several books and papers on this topic in my work.Of the set of standard references (Pearl, Jensen, Neapolitan, Jordan, Cowell et al., Borgelt & Kruse) the text by Korb and Nicholson (K&N) stands out in terms of its clarity and accessibility.Does the book have everything one would ever want to know about Bayesian inference?Not by a long shot.Is it, however, a good place to start?Definitely.The basic concepts are presented relatively completely and with clarity.I consistently recommend K&N over other alternatives to colleagues new to the field.Is there a chasm separating concept and algorithm in the book?I don't think there is, especially relative to other references.With tools such as Kevin Murphy's BNT, or Netica available on the Web, it seems to me that providing a solid conceptual framework becomes paramount for a textbook such as this.I believe K&N succeed admirably in this sense.Why four stars and not five?Even for an introductory text such as K&N, it would be nice to have more development of some concepts such as causality, context specific independence, or loss of independence in dynamic nets.Although it won't be your last book on reasoning with graphical models, K&N should probably be your first.

Bayesian Networks for Undergrads and Practicioners
Despite its name "Bayesian Artificial Intelligence" covers Bayesian network (BN) techniques only.Other Bayesian techniques useful for AI are not treated.
The content is divided in three main sections: (1) The basics of probabilistic reasoning with BNs, (2) Causal discovery (finding BNs from data), and (3) "Knowledgeengineering".
The first part covers the fundamental concepts and algorithms around BNs and (simple) decision networks.It is well-written and clear, but readers who are not totally new to the field might find only little new information (e.g., loopy belief propagation, continuous densities, large decision networks, etc. are not covered).
The second part is on how to deduce causal relationships from observational data.Constrained-based and Bayesian approaches are covered, but on a rather general level.I am not sure how easy it is to implement the algorithms from the descriptions provided.When it comes to details of the algorithms, proofs, or mathematical background the authors very often refer to the literature due to "lack of space".From a practical standpoint, it is unfortunate that the different methods are compared to each other only superfiscially.For instance, one method presented performs a large number of statistical tests; one would expect that this requires large amounts of data in order to avoid false positive results.Is this a problem? With questions like these the reader is often left alone.
I am not competent to talk about part three (knowledge engineering), so I end with my general impression of the book: I would have appreciated if the authors had treated some the algorithms in greater detail and had spent a few pages on advanced concepts and current research directions.On the other hand, some information provided could have easily been left out.(For instance, how to download and install certain software packages from the internet, Kevin Murphy's well-known survey on BN software packages, screenshots of user dialogs, etc. just eat pages.Providing the URLs to the corresponding sites on the internet is completely sufficient, and the information there is more likely to be up-to-date.)The saved pages could then be spent on information which is not readily available elsewhere.
To summarize: The book provides a mostly well-written general overview of the basic concepts and could serve as a first introduction to the field. However, it leaves the reader often alone when it comes to the mathematical background, potential practical pittfalls, or advanced algorithms. ... Read more

Product Description
The increasing trend of multimedia data use is likely to accelerate creating an urgent need of providing a clear means of capturing, storing, indexing, retrieving, analyzing, and summarizing data through image data.

Artificial Intelligence for Maximizing Content Based Image Retrieval discusses major aspects of content-based image retrieval (CBIR) using current technologies and applications within the artificial intelligence (AI) field. Providing state-of-the-art research from leading international experts, this book offers a theoretical perspective and practical solutions for academicians, researchers, and industry practitioners. ... Read more

Product Description
"Machines who think—how utterly preposterous," huff beleaguered humanists, defending their dwindling turf. "Artificial Intelligence—it's here and about to surpass our own," crow techno-visionaries, proclaiming dominion. It's so simple and obvious, each side maintains, only a fanatic could disagree.

Deciding where the truth lies between these two extremes is the main purpose of John Haugeland's marvelously lucid and witty book on what artificial intelligence is all about. Although presented entirely in non-technical terms, it neither oversimplifies the science nor evades the fundamental philosophical issues. Far from ducking the really hard questions, it takes them on, one by one.

Artificial intelligence, Haugeland notes, is based on a very good idea, which might well be right, and just as well might not. That idea, the idea that human thinking and machine computing are "radically the same," provides the central theme for his illuminating and provocative book about this exciting new field. After a brief but revealing digression in intellectual history, Haugeland systematically tackles such basic questions as: What is a computer really? How can a physical object "mean" anything? What are the options for computational organization? and What structures have been proposed and tried as actual scientific models for intelligence?

In a concluding chapter he takes up several outstanding problems and puzzles—including intelligence in action, imagery, feelings and personality—and their enigmatic prospects for solution.

A Bradford Book ... Read more

Customer Reviews (3)

Don't judge this book by its cover...
Don't judge this book by its cover-or at least by its title.Haugeland's Artificial Intelligence: The Very Idea does not adequately serve as a general introduction to the conceptual underpinnings and philosophical background of the quest to create an artificial mind.Rather, it focuses on one specific approach to how natural and man-made thought works: "thinking...essentially is rational manipulation of mental symbols." (p. 4)Haugeland plows forward with this as his core assumption, barely noting that some AI researchers see thought from a very different perspective (for example, the connectionists) and others find the whole enterprise fraught with theoretical difficulty (such as Dreyfus).

So Haugeland's story is that of a particular theory of mind that held predominance for several decades (what the author himself dubs "good, old-fashioned artificial intelligence" or "GOFAI", p. 112) but is now gradually being superceded.His introduction to this story concludes with a description of the Turing test and a justification for its use, and a brief statement of the efficacy of describing a system in different-even contradictory-ways through different "organizational levels".(p. 9)Of all the ideas presented in the book, this last one has the greatest promise for applicability beyond GOFAI.

Chapter 1, "The Saga of the Modern Mind", is a condensed bit of intellectual history.Haugeland introduces the philosophical children of the Copernican revolution-Hobbes, Descartes, and Hume-and the ways they grappled with understanding the world of the mental with the ideas that had proven so effective in the physical sciences.We soon encounter the "paradox of mechanical reason": if reason is the meaningful manipulation of symbols, and meanings are not physical entities, then how can machines manipulate them?(p. 39)

Chapter 2 serves as an extended definition of "Automatic Formal Systems", that is, computers.This material is the most challenging in the text, but the important concepts (formal games, digital systems, medium independence, etc.), are well-described, except for finite playability.The students I tutored through this work found it impossible to determine just what point was being made, and so did I.

How does one assign meanings-connections to the "real", outside world-to the symbols that a computer manipulates?This question is taken up in Chapter 3, "Semantics"-and answered, it seems, by sleight-of-hand.Haugeland gives to this the name "the formalist's motto": "if you take care of the syntax, the semantics will take care of itself".(p. 106)Neither I nor my students found this simple resolution at all satisfying.In every example of a formal game that the author presents, whatever semantic interpretation it has is provided from outside the system.

Chapter 4, "Computer Architecture", charts the milestones of computing.It begins with the analytical engine, and lauds Babbage's single-handed invention of programming without noting, however, that a human mind does not resemble the tabula rasa of a computer's memory bank.Moving quickly to the twentieth century, we get insightful descriptions of Turing machines, von Neumann machines (which turn out to be the kind of computer we are accustomed to), the mind-bending tree-structured LISP machines, and Newell's pragmatic production machines.

Chapter 5, "Real Machines", might be better titled "Real Problems".Haugeland presents some of the brick walls that AI research has run into.These can be grouped into the phenomenon of the combinatorial explosion:in order to interact with the real world in a manner that demonstrates "common sense", an AI must have access to an impossibly large store of information (while accessing what it needs in due time), and be able to consider an equally impossibly large set of potential courses of action.(p. 178)Methods to restrict what the AI has to consider, such as the focus on "micro-worlds", result in a system with no sense.Haugeland acknowledges these problems, and offers nothing but hope in scientific and technological progress to answer them.

Chapter 6, "Real People", develops means by which the sense that humans exhibit, and machines are far from realizing.Dennett's intentional stances and Grice's conversational implicatures are intelligent-if partial-characterizations of perspicuous reasoning.They are, however, frustratingly slippery for computer programmers, so it's not surprising that Haugeland, with some exasperation, groups them together under the "nonasininity canon": "An enduring system makes sense to the extent that, as understood, it isn't making [a rear] of itself."(p. 219)I feel that, if a reader has followed the author this far, then he or she deserves better than this.

Yet Haugeland and his colleagues are bound to feel frustration.Computers are electromechanical in nature, while humans are neurochemical.Computers can engage in numerical calculation with speed and precision, while most people find mathematics to be their most difficult school subject.Computers are tools that we devised to assist us.Human behavior was forged in the four-billion cauldron of evolution, and psychologists have barely begun to sort out the seething stew of vestigial loves, hates, and motivations that shape our behavior.And honest cognitive science will admit that humans and supercomputers are each masters of two separate, very different worlds.At the end, Haugeland finally admits this possibility-without contemplating the alternatives to the computation theory of might that this possibility demands.

THE VERY BEST ON CLASSICAL AI
This is the very best book on classical AI.However, there's a catch, as classical AI has many pitfalls, such as the frame problem or the symbol grounding problem.But there are ways to overcome these pitfalls, and ifyou want to see what's really hot in AI today you should check out DouglasHofstadter's Fluid Concepts and Creative Analogies.

A great exposition of the fundamentals and more.
This is a great exposition of the fundamentalnotions involved in the philosophy of AI. While at first look may appear like a good undergraduate read, it is, in fact, quite subtle and deep in most of the material ittouches. Great scholarship. ... Read more

Product Description
Russ Eberhart and Yuhui Shi have succeeded in integrating various natural and engineering disciplines to establish Computational Intelligence. This is the first comprehensive textbook, including lots of practical examples. -Shun-ichi Amari, RIKEN Brain Science Institute, Japan

This book is an excellent choice on its own, but, as in my case, will form the foundation for our advanced graduate courses in the CI disciplines. -James M. Keller, University of Missouri-Columbia

The excellent new book by Eberhart and Shi asserts that computational intelligence rests on a foundation of evolutionary computation. This refreshing view has set the book apart from other books on computational intelligence. The book has an emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. -Xin Yao, The Centre of Excellence for Research in Computational Intelligence and Applications, Birmingham

The "soft" analytic tools that comprise the field of computational intelligence have matured to the extent that they can, often in powerful combination with one another, form the foundation for a variety of solutions suitable for use by domain experts without extensive programming experience.

Computational Intelligence: Concepts to Implementations provides the conceptual and practical knowledge necessary to develop solutions of this kind. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational intelligence that has, in their extensive experience, proved highly effective.

Features
. Moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific concepts and paradigms that inform the authors' methodologies.

. Explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation.

. Details the metrics and analytical tools needed to assess the performance of computational intelligence tools.

. Concludes with a series of case studies that illustrate a wide range of successful applications.

. Presents code examples in C and C++.

. Provides, at the end of each chapter, review questions and exercises suitable for graduate students, as well as researchers and practitioners engaged in self-study.

. Makes available, on a companion website, a number of software implementations that can be adapted for real-world applications.

· Moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific concepts and paradigms that inform the authors' methodologies.

· Explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation.

· Details the metrics and analytical tools needed to assess the performance of computational intelligence tools.

· Concludes with a series of case studies that illustrate a wide range of successful applications.

· Presents code examples in C and C++.

· Provides, at the end of each chapter, review questions and exercises suitable for graduate students, as well as researchers and practitioners engaged in self-study.

· Makes available, on a companion website, a number of software implementations that can be adapted for real-world applications. ... Read more

Customer Reviews (3)

prominent acknowledgement of Hopfield
Perhaps the best section of the book was its coverage of the field's history. Minsky and Papert were mentioned as publishing a paper in 1969 that dumped on neural networks and led to a diminishing in funding. So much so that the book's authors call those years the Dark Age. It lasted till the 80s, when Hopfield published a series of seminal papers, that led to a revival. He took ideas from physics (especially solid state physics, which was his professional background) and applied them in novel ways to neural networks. To the extent that so-called Hopfield networks were subsequently described in many papers. This interdisciplinary mixing of physics and biology may prove inspirational to some readers doing active research.

Later parts of the book then explain the various types of neural networks currently in use. Along with sufficient details about implementation to aid you start up your work.

However, the book does [perhaps correctly] omit one thing. In the 80s, after Hopfield invigorated the subject, there was much speculation that the improved approaches might yield some qualitatively new and striking phenomena. Perhaps something even approaching a functioning, self-aware mind. Alas, this has not come to pass. Neural networks have certainly become an important and practical tool. But the excitement has died down.

Great intro for non mathematicians...
Being a programmer, I was looking for a good concept book that did not burry me in math formulas. I appreciate the implementation examples which enables me to understand the concepts in a form I understand better than formulaes...that is source code...

All in all a very nice book, well written and the supporting website is also first class...Good job...

not good enough
This book doesnt' have enough detail of neuron network. I have to buy another one for neuron net. However, Evolutionary Computation is good enough to read such as swarm or genetic algorithm. ... Read more

Product Description
This book is the first major text to encompass the wide diversity of geophysical applications of artificial neural networks (ANNs) and fuzzy logic (FZ). Each chapter, written by internationally-renowned experts in their field, represents a specific geophysical application, ranging from first-break picking and trace editing encountered in seismic exploration, through well-log lithology determination, to electromagnetic exploration and earthquake seismology.
The book offers a well-balanced division of contributions from industry and academia, and includes a comprehensive, up-to-date bibliography covering all major publications in geophysical applications of ANNs and FZ. A special feature of this volume is the preface written by Professor Fred Aminzadeh, eminent authority in the field of artificial intelligence and geophysics.
The enclosed CD-ROM contains full colour figures and searchable files, as well as short biographies of the editors. ... Read more

Product Description
A revision of an established text for undergraduate and postgraduate Artificial Intelligence courses, this text incorporates the latest research and methods. Special features include: a broad survey of AI methods; real-world examples and detailed algorithm descriptions, which are not language specific and which help students grasp the practical applications of AI theory; and a chapter on basic problem solving methods which shows students the major structures in which artificial intelligence programs can be built.Amazon.com Review
Artificial Intelligence is a somewhat datedintroduction to the subject. If you are looking for an introduction tocore topics in artificial intelligence (AI), such as logic, knowledgerepresentation, and search, this book has something to offer. However,if you want to learn about some of the newer areas of AI, such asgenetic algorithms, neural networks, and intelligent agents, you willwish to select a different text. ... Read more

Customer Reviews (5)

A really bad textbook.
This is one of the worst books I have actually seen on the subject of Artificial Intelligence. This book is aimed at both advanced undergraduate and graduate studies in AI. It does cover a large number of topics as expected by an introductory book on AI.

The main problem with this book is its use of language. The book tries to explain everything in formal english. This makes the explanations extremely hard to understand without rereading it a number of times. There is no time spent in giving explanations in simpler prose or resorting to mathematical formalism wherever needed. But instead the book reads like Principia Mathematica, except that the words used are familiar English words instead of Greek symbols.

Of course, a seasoned veteran of the subject can easily make sense of most of the things in the book. But the book is designed to throw off any new student of the subject. Unfortunately the book does not even work as a handy reference for a veteran. Finding stuff in the book does require a lot of reading through difficult prose.

Overall this is a bad book, both has an introductory text book and as a reference book. If you are looking for an AI textbook: I would highly recommend Artificial Intelligence: A Modern Approach by Russel & Norvig.

Very Crisp
It requires a number of readings to understand. No detailed examples or code for most of the topics. Not clearly explained the relation between Memory-based reasoning and case-based reasoning. If you are already havesome knowledge in AI or if you want to know in some detail of varioustopics of AI then this is a good book. Probably Nils J Nilson's AI book maybe a good one to start and then use this book.

Good basic introduction, but little else.
I am a games programmer who was wanting to get better understanding of some artificial intelligence applications and theory. This book provided a reasonable introduction, but very little more than I had picked up from myown experience. Search algorithms, State spaces, goal oriented planning andall the basics are covered, but it doesn't go much farther from there. Ifyou know NOTHING about AI it could be a useful addition to your library,but if you're even a novice like myself with introductory understanding itprobably won't offer you anything new.

A MUST BE for the AI interested.
This book is one of the best books I've seen in AI field. Our instructor recommended this book, and I found that he was right in his opinion. This is a MUST BE textbook for every teachers (students) who want to teach(learn) AI to be a good one in AI field

Great Introduction
This is the best introduction about AI I know. It is well readable but still gives precise information. I recommed this as yout first book on AI (second one could be e. g. Winstons AI-Book). ... Read more

Product Description

Offering a wide range of programming examples implemented in MATLAB^®, Computational Intelligence Paradigms: Theory and Applications Using MATLAB^® presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence. It covers numerous intelligent computing methodologies and algorithms used in CI research.

The book first focuses on neural networks, including common artificial neural networks; neural networks based on data classification, data association, and data conceptualization; and real-world applications of neural networks. It then discusses fuzzy sets, fuzzy rules, applications of fuzzy systems, and different types of fused neuro-fuzzy systems, before providing MATLAB illustrations of ANFIS, classification and regression trees, fuzzy c-means clustering algorithms, fuzzy ART map, and Takagi–Sugeno inference systems. The authors also describe the history, advantages, and disadvantages of evolutionary computation and include solved MATLAB programs to illustrate the implementation of evolutionary computation in various problems. After exploring the operators and parameters of genetic algorithms, they cover the steps and MATLAB routines of genetic programming. The final chapter introduces swarm intelligence and its applications, particle swarm optimization, and ant colony optimization.

Full of worked examples and end-of-chapter questions, this comprehensive book explains how to use MATLAB to implement CI techniques for the solution of biological problems. It will help readers with their work on evolution dynamics, self-organization, natural and artificial morphogenesis, emergent collective behaviors, swarm intelligence, evolutionary strategies, genetic programming, and the evolution of social behaviors.

Product Description
Stochastic local search (SLS) algorithms are among the most prominent and successful techniques for solving computationally difficult problems in many areas of computer science and operations research, including propositional satisfiability, constraint satisfaction, routing, and scheduling. SLS algorithms have also become increasingly popular for solving challenging combinatorial problems in many application areas, such as e-commerce and bioinformatics.

Hoos and Stützle offer the first systematic and unified treatment of SLS algorithms. In this groundbreaking new book, they examine the general concepts and specific instances of SLS algorithms and carefully consider their development, analysis and application. The discussion focuses on the most successful SLS methods and explores their underlying principles, properties, and features. This book gives hands-on experience with some of the most widely used search techniques, and provides readers with the necessary understanding and skills to use this powerful tool.

*Provides the first unified view of the field.
*Offers an extensive review of state-of-the-art stochastic local search algorithms and their applications.
*Presents and applies an advanced empirical methodology for analyzing the behavior of SLS algorithms.
*A companion website offers lecture slides as well as source code and Java applets for exploring and demonstrating SLS algorithms. ... Read more

Customer Reviews (1)

The First Complete Solution
The Travelling Salesman Problem (more politically correctly called the Travelling Salesperson Problem)(How about we call it the TSP?) is a common real world problem. The problem is simplely stated: How do you find the shortest path for a travelling sales_____ to drive as he visits a series of customers.

If you're just running a few errands it's easy enough. If you're going to the supermarket, the post office, the dry cleaners, and the gas station, it's pretty easy to determine the shortest path. But then you add constraints, nearly out of gas, go to gas station first. Buying ice cream at the super market, better make it the last stop.

This is a real world problem. It is faced every day by delivery companies like the post office and air freight companies who spend huge amounts of fuel flying jet freighters around the world -- where do you put a hub, how do you schedule everything to come together.

FedEx solved this problem by running everything through Memphis. This works really well for a package going from New York to LA. It works less well for a package going from Manhattan to the Bronx. And if the package is going from London to Manchester ....

The computation of this and similar problems fall into the general category of Stochastic Local Search. And this book is the first to offer a systematic and unified treatment of SLS. Before this there were a series of technical papers, magazine articles and chapters in more general texts. The book provides the first unified view of the entire field and offers an extensive review of state-of-the-art algorithms and their applications. A companion website offers lecture slides as well as source code and Java applets for exploring and demonstrating the algorithms. ... Read more

Product Description
Computational Intelligence: A Logical Approach provides a unique and integrated introduction to artificial intelligence. It weaves a unifying theme--an intelligent agent acting in its environment-- through the core issues of AI, placing them into a coherent framework. Rather than giving a surface treatment of an overwhelming number of topics, it covers fundamental concepts in depth, providing a foundation on which students can build an understanding of modern AI. This logical approach clarifies and integrates representation and reasoning fundamentals, leading students from simple to complex ideas with clear motivation. The authors develop AI representation schemes and describe their uses for diverse applications, from autonomous robots to diagnostic assistants to infobots that find information in rich information sources.Ideal for upper-level undergraduate and introductory graduate courses in artificial intelligence, Computational Intelligence encourages students to explore, implement, and experiment with a series of progressively richer representations that capture the essential features of more and more demanding tasks and environments. ... Read more

Customer Reviews (4)

Buy A Better Book
This is by far the worst book I've ever read in my college career.Throughout the entire book only two to three main examples are used.Many times the examples are not carried along through the text appropriately and the reader is referred back to previous pages with information that doesn't really help.And, I've found at least one instance where the reader is referred back to an example and then referred back yet again to a different page. Not good.

I would give this book less than one star if I could.

Cretenous.
The text is cretenous. Videlicet:

- Many critical concepts have their wording arranged in a rather obscure fashions. So many things could have been explained using a far simpler description.

- Almost all of the examples given use the exact same retarded office delivery robot context.

- There are no solutions provided to any of the problems at the end of each chapter. Thus, the problems serve absolutely no functional value whatsoever as training aids because they are unable to advise when the student errs.

- As the title suggests, the text only covers the logical approach to computational intelligence using Prolog and its various flavours. There are no examples of imperative implementations using, say, a genetic algorithm. While I have heard some say that having the theory will allow you to implement in any manner, I dismiss this as nonsense. If that was the case, it would be more rational to learn the material via languages like C and C++ that most are already familiar with, and then, if necessary, implement in Prolog or some other obscure language.

- This text would be fine if it were used only in survey courses where an intimate understanding of every detail was unnecessary. Sadly, it is used in upper level university AI courses.

- There is typically but one example provided for each concept. If the example doesn't make sense, the concept won't either unless you search in other books or on the internet for other examples using the same concepts.

- This book is far too expensive for what it is worth. I would suggest picking up a copy of "AI Techniques for Game Programmers". It doesn't waste any time with Prolog or any other purely academic radical development paradigms, but remains mindful of the real world.

shame on the Mackworth and Poole
I was a student of Dr. Poole's ( one of the co-authors ) at the University of British Columbia and was forced to use this textbook for two semesters.It is without doubt the worst textbook on any subject in Computer Science that I have ever read. The book is extremely vague and confusing on many important subjects. The book also uses unnecessarily complex wording to describe simple concepts .. at some times it is much like reading code.

Serves well as an introduction
Everything in this book used to be classified as artificial intelligence, but the authors have chosen to call it computational intelligence, arguing that it is the computational aspects of the subject that they want to emphasize. The book is very well written, and students and those interested in A.I. research and development will find it a helpful step to more involved studies.

The emphasis in the book is on intelligent agents, which the authors characterize in chapter one. Agents are viewed as black boxes that take in knowledge, past experiences, goals/values, and observations and output actions. They define what they call a representation and reasoning system consisting of a language to communicate to a computer, a methodology for giving meaning to this language, and a collection of procedures for computation. They also outline the three applications domains they will be developing in the book: an autonomous delivery robot, a diagnostic assistant, and an infobot.

The authors expand upon the representation and reasoning system in chapter 2 in terms that are familiar from mathematical logic and computer science. A formal language, a semantics, and a proof procedure are the three essentials of an RRS. All of these elements are discussed in great detail, and concrete examples are given for all the main concepts. Readers without any background in logic may find the reading difficult, but with some effort it could be read profitably. The authors do a good job of presenting material that is usually delegated to texts on formal computer science.

In chapter three, the authors show how representational knowledge can be used for domain representation, querying, and problem solving. This is done via an example of electrical house wiring and the PROLOG-astute reader will find the presentation very straightforward. But LISP programmers will also see its influence and the discussion on lists. An application is given in computational linguistics, namely that of definite clauses for context-free grammars.

A discussion of searching is given in chapter 4, in the context of potential partial solutions to a problem, with the hope that these will truly be real solutions for the problem at hand. Graph searching, blind search strategies, heuristic searching, and refinements of these are all discussed with great clarity. And, because of their importance in applications, dynamic programming and constraint classification problems are overviewed, albeit very briefly.

Chapter 5 turns to the topic of how to choose a representation langauge for knowledge. The authors detail the criteria for comparing different languages or logics in terms of expressiveness, worse-case complexity, and naturalness. Most important in this chapter is the discussion on qualitative versus quantitative representations.

This is followed in chapter 6 by a discussion of the user interactions to a knowledge-based system in terms of a meta-interpreter that produces knowledge acquistion, debugging, etc.

The next chapter shows how definite clause representation and reasoning systems can be extended to include the relation of equality and negation, and quantification of variables. This sets up naturally a discussion of first-order predicate calculus, but only a brief overview is given. A very short treatment of modal logic is given.

Chapter 8 considers agents that act and reason in time, with three representations given for reasoning about time. These are the STRIPS representation (developed at Stanford University), the situation calculus, and the event calculus. It is then shown how these can be used to reason and produce plans to achieve goals. Although brief, the discussion is very interesting, and the authors give good references for further reading.

The authors generalize their discussions to assumption-based reasoning in chapter 9, which up until this chapter has been restricted to reasoning from knowledge bases. Nonmonotonic reasoning is defined, along with abduction, which is a form of reasoning different from both deduction and induction, and which emphasizes hypothesis formation.

Chapter 10 considers the more realistic situation whre the agents have incomplete or uncertain knowledge. This naturally brings up a discussion of probability, which the authors define as the study of how knowledge affects belief. They distinguish between evidence and background knowledge, the latter which is stated in terms of conditional probabilities, the former characterized by what is true in the situation being studied. Belief networks are introduced as a graphical representation of conditional independence, these graphs being directed and also acyclic (the latter for reasons of causality). An algorithm for determining the posterior distribution of belief networks is given, and is based on the idea that a belief network specifies a factorization of the joint probability distribution. A brief overview of decision networks is also given.

The important topic of learning theory is overviewed in chapter 11. And, naturally, neural networks make their appearance here, although the discussion is very brief. PAC learning is also treated, as well as Bayesian learning. Unfortunately, the important field of inductive logic programming is not discussed, but some references are given.

The last chapter covers artificial purposive agents, otherwise known as robots. This is a vast subject, and only a general overview is given here, but the authors do a good job of showing how robots can be characterized within the concepts outlined in the book. Dynamical systems are used to represent the agent function for a robot. Readers familiar with the theory of dynamical systems will see the state transition function appear here in a more general context. The states of an agent at time t encode all of the information about its history. The state transition functions acts on the states and percepts, with the percepts playing the role of time in the usual dynamical system.

The appendices include a terminology list and a short introduction to PROLOG, along with a few examples of PROLOG code applied to some of the concepts in the book. Although very general, the inclusion of these examples are of further help in understanding the material in the book. ... Read more

Product Description
* Not for sale in the U.S. and Canada ... Read more

Product Description

In this accessible, comprehensive text, George Luger captures the essence of artificial intelligence–solving the complex problems that arise wherever computer technology is applied. Key representation techniques including logic, semantic and connectionist networks, graphical models, and many more are introduced. Presentation of agent technology and the use of ontologies are added. A new machine-learning chapter is based on stochastic methods, including first-order Bayesian networks, variants of hidden Markov models, inference with Markov random fields and loopy belief propagation. A new presentation of parameter fitting with expectation maximization learning and structure learning using Markov chain Monte Carlo sampling. Use of Markov decision processes in reinforcement learning. Natural language processing with dynamic programming (the Earley parser) and other probabilistic parsing techniques including Viterbi, are added. A new supplemental programming book is available online and in print: AI Algorithms in Prolog, Lisp and Java ™. References and citations are updated throughout the Sixth Edition. For all readers interested in artificial intelligence.

Customer Reviews (9)

good mention of Hidden Markov Models
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.

Superficial and unclear
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 andthe number of pages, and try to see how much space can be given toevery point... not enough.

Fantastic Introduction to 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!

this book not cover much
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.

Good For Beginners in AI
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. ... Read more

Product Description
Artificial intelligence (AI) is a field within computer science that is attempting to build enhanced intelligence into computer systems. This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today's AI engineers. AI is becoming more and more a part of everyone's life. The technology is already embedded in face-recognizing cameras, speech-recognition software, Internet search engines, and health-care robots, among other applications. The book's many diagrams and easy-to-understand descriptions of AI programs will help the casual reader gain an understanding of how these and other AI systems actually work. Its thorough (but unobtrusive) end-of-chapter notes containing citations to important source materials will be of great use to AI scholars and researchers. This book promises to be the definitive history of a field that has captivated the imaginations of scientists, philosophers, and writers for centuries. ... Read more

Customer Reviews (4)

History of a Remarkable Technology
This is an extremely literate, well written history of the first fifty years of AI by someone who fortuitously came in on the ground floor of this field.Nilsson's perspective is unique and invaluable for anyone interested in broadening their horizons, and in appreciating how many talented and driven individuals have contributed to AI's successes.

As a lay reader, I skipped the notes and many of the technical details and diagrams.I enjoyed the many interesting references scattered through the text. Just to give a flavor of these, in the first chapter alone there are references to Homer's "Iliad", Ovid's "Metamorphosis", The Talmud, opera (Offenbach's "Tales of Hoffman"), and theater, Capek's "R.U.R."I won't mention more of them here but leave them for you to discover, choice morsels all.Although this is a scholarly work, it's accessible to anyone who is interested in what AI is all about.
AI has already become an integral part of our lives.It's used for computing driving directions, interactive computer games, aircraft control, credit card fraud detection, vending machine currency recognition, robot control, speech recognition, and face identification, to name just some of the more prominent examples.

I came away marveling at how far this field has come in 50 years and convinced of the need for more basic research.Most of the important inventions were due to basic research.At the time, the results, to an untrained eye, looked stunningly simple.People thought, "What good is that?" We're now reaping the harvest of those years of early work, and one hopes that, along with applications, basic research in the field will continue.

This book is a significant contribution to the history of science.

recommended!
There is a great deal of good material here. I wonder if the general problem of producing a history of AI would not have been better decomposed into a set of mini-histories each concerned with a particular topic and occupying a single chapter. For example, NLP, machine vision, robotics, knowledge representation, vagaries of public/private financing, major commercial deployments, etc. each could have been addressed in a single chapter. In this book, individual topics pop up again and again in interleaved fashion as the author's single timeline unfolds. This may be a bit disconcerting for readers not already well versed in the field.Other advantages of a modular approach to the history of AI rather than a simple sequential approach are ease of updating the text for future editions and the ability of subject matter experts to quickly find and provide constructive feedback in their areas of expertise.

A minor irritation was the use of URLs in body text rather than confining them to end notes. Most authors would like their books to be timeless; the use of highly fragile URLs in body text seems to contradict this goal.

I suspect that this is the best history of AI we have so far. I recommend this book to anyone interested in the field.

An engaging, accessible and definitive history of artificial intelligence
Nils J. Nilsson's book begins with the story of how artificial intelligence originated in 1956 at a Dartmouth summer project that had the goal of "making a machine behave in ways that would be called intelligent if a human were so behaving." It relates how inthe fifty-plus years that followed, AI has been the subject of overly-optimistic predictions, academic arguments that its goals are unachievable, funding excesses, and funding droughts.But the underlying reality is that AI has contributed key components to the technology foundations that shaped the modern world, and indeed has transformed our view of machines and of our relation to them.

The algorithms that compute your driving directions, and also compute the paths of characters in video games? They rely on results from AI research on mobile, intelligent robots.Those surprisingly high-quality voice response systems we encounter when we phone a customer-service number?They use results from AI research in speech recognition. The recommender systems ("You might also like") used by many web vendors? They use machine learning methods whose history is described by Nilsson. And AI technology is embedded in a host of less-apparent applications ranging from medical devices to automated securities trading systems.

Nils J. Nilsson's comprehensive account of the evolution of AI covers the field from its inception to recent times.All the major sub-fields of AI receive attention--from game playing to automatic problem solving, from computer vision to speech and language understanding, from expert systems to machine learning and probabilistic reasoning--allthese and more are covered.

Nilsson enriches his account by viewing major developments through a multi-faceted prism. He describes AI's challenges, the approaches adopted and the landmark systems in just enough detail to give the reader real insight into the technical substance of the field.He also describes the funding issues and controversies that have swirled around AI since that very first Dartmouth meeting.And he introduces the reader to scores of brilliant, frequently colorful, characters whose contributions and opinions have influenced the course of developments.

For the AI practitioner, this book is a rare example of that often proclaimed, but seldom sighted species, the "essential volume" for your library.Your perspective on AI cannot help but be enhanced; you'll gain an increased appreciation for the time it takes for a good idea to mature and find a place in the world; and you may even be encouraged to revisit nearly-forgotten ideas that have relevance to current research issues.

But the book has appeal for the general reader as well.Nilsson is a masterful teacher and storyteller, and his description of timeless philosophical issues and intellectual challenges are as clear as you will find in as confined a space.Technical approaches are profusely illustrated and diagrammed, but remain accessible to any reader with an active curiosity.The tone of the book is straightforward and conversational, with neither the stuffiness of a self-important academic nor the breeziness of a science popularizer.

Predictions about AI have proven hazardous for 50 years, but I'll make one here:It will be a long time before any writer attempts a sequel to this unique and valuable volume.

Accessible to everyone- A lucid account of how AI has become a pervasive part of our lives
Professor Nilsson's humanistic account of the dreamy beginnings: Venus bringing an ivory statue of a beautiful maiden to life, "The girl felt the kisses he gave, blushed, and raising her bashful eyes to the light, saw both her lover and the sky"; and continuing on in an intuitive sequence from "early explorations" to "the quest toward human-level Artificial Intelligence" is a readily readable and engaging tour de force.

The scope is grand and encyclopedic; it is the culmination of a lifetime of brilliant scholarship and acclaimed teaching. ... Read more

Product Description

Argumentation provides tools for designing, implementing and analyzing sophisticated forms of interaction among rational agents. It has made a solid contribution to the practice of multiagent dialogues. Application domains include: legal disputes, business negotiation, labor disputes, team formation, scientific inquiry, deliberative democracy, ontology reconciliation, risk analysis, scheduling, and logistics.

This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Argumentation in Multi-Agent Systems held in Hakodate, Japan, in May 2006 as an associated event of AAMAS 2006, the main international conference on autonomous agents and multi-agent systems.

The volume opens with an original state-of-the-art survey paper presenting the current research and offering a comprehensive and up-to-date overview of this rapidly evolving area. The 11 revised articles that follow were carefully reviewed and selected from the most significant workshop contributions, augmented with papers from the AAMAS 2006 main conference, as well as from ECAI 2006, the biennial European Conference on Artificial Intelligence.

Product Description

This volume presents the latest developments in the growing area of research at the interface of argumentation theory and multiagent systems.

Argumentation provides tools for designing, implementing and analyzing sophisticated forms of interaction among rational agents. Application domains include: legal disputes, business negotiation, labor disputes, team formation, scientific inquiry, deliberative democracy, ontology reconciliation, risk analysis, scheduling, and logistics.

The papers presented in this book constitute the thoroughly refereed post-workshop proceedings of the 4th International Workshop on Argumentation in Multi-Agent Systems, held in Honolulu, HI, USA, in May 2007 as an associated event of AAMAS 2007, the main international conference on autonomous agents and multi-agent systems.

A number of invited revised papers on argumentation in MAS are also included, from both AAMAS 2007 and AAAI 2007, the 22nd Conference on Artificial Intelligence. The book has been divided into three parts, each addressing an important problem in argumentation and multiagent systems. The first two parts focus on issues pertaining to dialogue and on using argumentation to automate or support various single agent reasoning tasks. The third part addresses an exciting new area in argumentation research, namely, the relationship between models of argumentation and models of learning.