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

Creating robust artificial intelligence is one of the greatest challenges for game developers. The commercial success of a game is often dependent upon the quality of the AI, yet the engineering of AI is often begun late in the development process and is frequently misunderstood.

Customer Reviews (6)

Dissapointing
I bought this book for an Artificial Intelligence for Games class at my University.I haven't read through all of the book, but I can already tell you that the pseudo-code in this book is very poor.It's obvious that the author didn't actually go through and run the code to make sure it worked.In the movement algorithms, the code would sometimes alter rotation (speed of orientation) when it should be altering the orientation itself.In the dynamic kinematics class, the code multiplies the velocity by the acceleration instead of just simply adding the acceleration to the velocity.

Even when you get the provided movement algorithms to work the way the author probably intended, there are still issues that aren't considered.I won't get into too much detail but an example is the "Arrive" behavior.It doesn't work properly because the bot never arrives at it's target.There is nothing in the algorithm that actually decelerates the bot or nullifies the velocity.So you basically get a bot that wiggles back and forth on stationary targets.

The explanations are pretty straight foward, and I admit I haven't read the full book yet.I just think it's pretty unacceptable to publish something with so many errors in the pseudo-code.

Great academic approach of AI
This book is really good and is different from other ones in the field of Artificial Intelligence. Millington explains difficult stuff in an easy and readable way. I like the academic approach of the book, I used it during my last year in college and it turned out really useful. If you want implementation details you have the source code in c++. The use of pseudocode is the best idea when writing these sort of books.

Excellent C++ Source for AI
This is a very solid book on AI for games.

The C++ source code provided with the book is excellent.While the examples are visually unexciting, they demonstrate the power of the book's principles without the clutter that a complete graphics game would require.I was able to compile and build all the examples on the CD in one evening. The code demonstrates many of the best practices of C++ programming and design patterns.

The author builds up a nice AI engine as you progress through the book.The C++ code from the CD (or web-site) is well commented and ties exactly into the pseudocode in the book.

Millington goes into considerable detail as he reveals the power of Artificial Intelligence for Games. He carefully explains each step including the math and physics required to carry out the execution. It is obvious that he has a great deal of experience in writing computer games. He shows you a clear solid way of doing things and then discussed the strengths and weaknesses by comparing it to other techniques and addressing possible optimizations.

To read and understand this book takes time and hard work. Artificial Intelligence is a large and complex topic in math and computer science programs.The author has brought many nuggets of wisdom from that branch of research and made them understandable and useful for game programmers.Not an easy job, but Millington is one of the best at explaining difficult concepts in a clear and straight forward way.

The other reviewer's that are knocking this book because of the code, don't knowwhat they are talking about. The code is excellent and what makes this a 5-star pick.

Powerful Concepts Made Easy
Understand that the pseudo-code approach this book takes is what makes it such a standout from the rest of the crowd. The author is technically thorough and the syntax is straightforward enough to use in any language needed. Moreover, it frees the author to discuss AI in abstract terms which, in the end, proves to be much more valuable content. C++ source code puts the pseudo-code discussions into practice for those looking for real-world examples.

I would HIGHLY recommend this book as a follow up to Mat Buckland's "Programming Game AI by Example" (Nov., 2004)

Not a great source for code
The author uses "pseudo-code" through out the book. The cd contains only a pc-executable program. There is no source code on the CD.

This book is a poor source of programming code where the author explains how ai works based on the pseudo-code.

If you're looking for source code (ie C++ source code) you'll not find it here. ... Read more

Product Description
This is a practical, highly-accessible introduction to the state-of-the-art in artificial intelligence.This book demystifies artificial intelligence, making it concrete and transparent. It covers knowledge representation, inference, expert systems, natural language processing, machine learning, neural networks, agents, robots, and more. The book includes extensive self-test questions, case studies, figures, worked examples, sample algorithms and a complete glossary.For anyone interested in artificial intelligence; no prior background is required. ... Read more

Customer Reviews (5)

A good overview and introduction to the field of AI
This book a a great starting point for studying Artificial Intelligence.For those with a Computer Science background, the book is a quick read that will show how theories such as data structures and search algorithms apply to the different areas of AI.For those without a background in computers, the book will take longer to read and for deeper understanding of some subjects other texts may need to be consulted.However, it is still one of the easiest-to-understand books on AI as most are extemely lengthy and detailed beyond the scope of what most beginners are able to understand.
The book is well written and explains complicated topics in plain English.Figures are used effectively to explain certain concepts.An extremely helpful feature is that every chapter is summarized and further references on that topic are given with a short description of the strength and weaknesses of each reference.
I would definitely recommend this book to those who want to learn about AI.Its a great starting point that can lead you in the right direction if you want to study a particular topic in further detail.

Wonderfully simple and sweet
This is a wonderfully compact introduction to the basic concepts of Artifical Intelligence. You probably aren't going to be able to go and write your own AI after reading this but at least you'll have enough background to read a more detailed text and some of the scientific literature out there. If you've picked up other AI books and felt lost then start here, you won't regret it.

Very readable introductory text
This is a very readable introductory text.Its coverage of topics is surprisingly good for such a slender volume.I especially liked the chapter on searching--the examples are very clear.

A neat and concise summary
This book is a fine introductory text on AI. It covers all major subjects in the field and it is very clear and elaborates on the problems in a very direct and simple manner.If you are looking for an introductory text,then you found it by now.

A neat and concise summary
This book is a fine introductory text on AI. It covers all major subjects in the field and it is very clear and elaborates on the problems in a very direct and simple manner.A very fine book as an introductory text. ... Read more

Product Description

This volume is a systematic, expansive presentation of the major achievements in the intersection between two fields of inquiry: Argumentation Theory and Artificial Intelligence. Contributions from international researchers who have helped shape this dynamic area offer a progressive development of intuitions, ideas and techniques, from philosophical backgrounds, to abstract argument systems, to computing arguments, to the appearance of applications producing innovative results. Each chapter features extensive examples to ensure that readers develop the right intuitions before they move from one topic to another.

In particular, the book exhibits an overview of key concepts in Argumentation Theory and of formal models of Argumentation in AI. After laying a strong foundation by covering the fundamentals of argumentation and formal argument modeling, the book expands its focus to more specialized topics, such as algorithmic issues, argumentation in multi-agent systems, and strategic aspects of argumentation. Finally, as a coda, the book explores some practical applications of argumentation in AI and applications of AI in argumentation.

Argumentation in Artificial Intelligence is sure to become an essential resource for graduate students and researchers working in Autonomous Agents, AI and Law, Logic in Computer Science, Electronic Governance, and Multi-agent Systems. The book is suitable both as a comprehensive introduction to the field, and also as a highly organized and accessible reference for established researchers.

Product Description
This comprehensive, easy-to-read survey of how machines (computers) can be made to act intelligently explores problem-solving methods, representation and models, game playing, automated understanding of natural languages, heuristic scene analysis, specific artificial intelligence accomplishments and other related topics. With 132 illustrations.
... Read more

Customer Reviews (6)

Good, but somewhat outdated
This is an interesting introduction to artificial intelligence, but it is rather outdated.In addition, while it gives a general overview of the entire field (at least how the field stood during the writing of the book), it doesn't give as man concrete examples, or as many code examples, as an in-depth developer might want.I would recommend Russell & Norvig's Artificial Intelligence: A Modern Approach for the serious developer, and forego this guy.

A good introduction book for grown-ups
I was thinking of purchasing an introductory book on AI for my 14 year old son since he was so interested in robots and automation. Apparently, this book is beyond him. I am not sure whether there is an AI book for children.

A Little dated, but very good introduction
Having last been printed in the mid 80's some of the information is getting a little dated at this point, but for anyone new to the subject it is a very good read and an excellent introduction to the feild of AI.

Great read, excellent price
I actually picked up this book at the discount bin at a local bookstore.I had always been interested in A.I research, and this deal was irresistable.However, I think this book is worth alot more, and provides more insight into the field than many of the current popular books on the subject.

This book basically goes into A.I research and leaves alot of the philosophical issues at a minimum.Basically you can look at this as a real text book about the subject of A.I.By my expirience, it isn't easy to find outside of the popular science market.

The topics that this book covers is extensive.The first few chapters go into subjects like Game Theory, and the problem-state models of A.I.He also gives a very extensive overview of the contruction of the human brain and its paralells to finite state machines.What I found particularly interesting was his coverage of many Turning Machines.Later, the author takes you into more rigorous examples dealing with problems of Theorem proving.And definitely one of the most interesting chapters was his coverage of natural languages.

I have owned this book for about 2 years, and although I do not read it faithfully everyday, I do find myself reading this book extensively for periods of 2-3 months.The material will demand a great deal of work on the behalf of the reader.As this book deals with many abstract concepts in mathematics that can be confusing to the untrained reader.Admitedly, i had to stop reading this book for a little while and take 4 months to get to a functional level of linear algebra, before I could fully comprehend the tranformation he showed chapter 6.

This is a must buy for anyone who wants to get their feet wet in the field of A.I.And with such a small price tag, you really cant lose.

Great Introduction and not only that.
I was searching for a book that will introduce me to artificial intelligence concepts; and although this book seemed old (1985), I bought it because of it's low price. Then when I opened it for the first time Iwas amazed how great it is. It worths a whole lot more. I soon found outthat some concepts are for ever, and no matter how old they will be currentin the future. ... 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
An inadequate infrastructure for software testing is causing major losses to the world economy. The characteristics of software quality problems are quite similar to other tasks successfully tackled by artificial intelligence techniques. The aims of this book are to present state-of-the-art applications of artificial intelligence and data mining methods to quality assurance of complex software systems, and to encourage further research in this important and challenging area. ... Read more

Product Description

Artificial Intelligence: The Basics is a concise and jargon-free introduction to the fast moving world of AI. Examining the modern origins of artificial intelligence, this book explores issues of what it means to be man or machine and looks at advances in robotics which have blurred the boundaries. Topics considered include:

• How intelligence can be defined
• Whether machines can 'think'
• The nature of consciousness

Exploring issues at the heart of the subject, this book is suitable for anyone interested in AI, and provides an illuminating and accessible introduction to this fascinating subject.

Product Description
This book constitutes the refereed proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning, ECSQARU-FAPR'97, held in Bad Honnef, Germany, in June 1997. The volume presents 33 revised full papers carefully selected for inclusion in the book by the program committee as well as 12 invited contributions. Among the various aspects of human practical reasoning addressed in the papers are nonmonotonic logics, default reasoning, modal logics, belief function theory, Bayesian networks, fuzzy logic, possibility theory, inference algorithms, dynamic reasoning with partial models, and user modeling approaches. ... Read more

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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.
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Law and legal reasoning are a natural target for artificial intelligence systems. Like medical diagnosis and other tasks for expert systems, legal analysis is a matter of interpreting data in terms of higher-level concepts. But in law the data are more like those for a system aimed at understanding natural language: they tell a story about human events that may lead to a lawsuit. Statements of the law, too, are written in natural language and legal arguments are often arguments about what that language means or ought to mean.This study is one of the few research efforts in this fertile area. It is unique in developing a computational model for analyzing legal problems in a way that brings these strands of AI research together and makes sense from a jurisprudential perspective as well.Gardner first analyzes several positions in Anglo-American jurisprudence and their relevance for work in artificial intelligence. She identifies aspects of legal reasoning that any truly expert system in law must make a place for and suggests a way of decomposing the process of legal analysis that takes these aspects into account. She compares the resulting framework with those used by other legal analysis programs. A solid exposition of current AI techniques follows in chapters covering the author's system (written in Maclisp) for offer and acceptance problems, taken from law examinations, involved in contract law.Anne von der Lieth Gardner has a law degree and a Ph.D. in computer science, both from Stanford University. An Artificial Intelligence Approach to Legal Reasoning inaugurates the series Artificial Intelligence and the Law: Processes and Models of Legal Reasoning, edited by L. Thorne McCarty and Edwina L. Rissland. A Bradford Book. ... Read more

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Of historical importance
To design a machine that can engage in legal reasoning has been of great interest in the field of artificial intelligence and in some schools of jurisprudence. This goal has not been achieved to the satisfaction of all those involved in building legal reasoning machines, but some progress has been made. This book, which is widely cited by those working in legal artificial intelligence, was one of the few at the time of publication that gave a fresh approach to the problem.

When reading the book it is apparent that many questions must be answered before a successful legal machine can be constructed. These include: How does one apply a rule to the stated facts of a legal case? Is there a demarcation between the conclusions that can be reached using ordinary logical deduction and those arrived at by the discretion of the judge? Can a machine analyze full and encapsulate in its knowledge base the concepts of wisdom and justice? How does the language of rules connect with the language in which facts are stated? What kinds of predicates are to be used only in the antecedents of rules? If the descriptions and examples are only `usually fairly good,' when can a machine make the conclusion that these examples are good enough for a particular issue at hand? How does one determine that a legal predicate not defined further by rules is clearly satisfied by the facts of a case being analyzed? How are past cases to be represented? How is the legal machine to represent the reason(s) for a decision? Which facts are to be considered relevant in determining the satisfaction of which legal predicate?

The author addresses these questions in this book, and even a reader not interested in the applications of artificial intelligence will gain good insights into the processes of legal reasoning. Legal conclusions for example can be divided into two classes, those that are the result of deductive reasoning and those that require the judge to select the `just' conclusion. A `just' conclusion is therefore to be distinguished from those arrived at deductively. This observation, if valid, definitely has ramifications for the building of legal machines, since deductive reasoning patterns are fairly easy to implement in machines. But the concept of a `just' conclusion would be a challenge for a machine implementation.

As brought out in the book, any kind of reasoning pattern utilized by a machine must be subject to constraints, these constraints being unique to the domain in which the machine reasons. In legal reasoning, this constraint takes the form of `stare decisis', which means that the machine must be able to make analogies and be aware of cases in the past. In addition, legal reasoning is `rule-guided', rather than rule-governed, and legal rules are heuristic in nature, generally have exceptions, and sometimes may contradict one another. Besides these constraints, the terms in legal discourse are what the author calls `open-textured,' in that the meaning of terms and predicates are inherently indeterminable. Legal questions frequently invite more than one answer, and these answers can change over time. Hence legal reasoning patterns must be able to adapt to a dynamic knowledge base.

According to the author, the strategy for a successful legal reasoning machine would involve the ability to distinguish between `hard' versus `easy' questions. The hard questions in legal discourse arise because of the existence of competing rules, unresolved predicates, and competing cases. The machine must be able to detect `hard' cases, and it could do this by using a collection of heuristics. One of these heuristics involves the use of what the author calls `common sense knowledge' (CSK) rules, which are to be distinguished from general human commonsense knowledge. If an answer can be derived using CSK rules and if there does not exist any objection to using this answer, then question is assumed to be `easy.' The second heuristic entails that if no answer about the satisfaction of a legal predicate can be defined using CSK rules, then the machine will search for cases that illustrate that the facts of the case at hand are actually an example of a situation that the legal predicate has covered in the past. The third states that if a tentative answer is derived using non-legal knowledge, then the machine will search for cases that call for the opposite answer.

To test and benchmark her strategy, the author works in the field of `offer and acceptance' and `contract law', and deals specifically with the case `Adams vs Lindsell'. To construct the reasoning patterns, she brings in a highly interesting construction that she calls a `augmented transition network' (ATN). An ATN represents the standard states in a contract situation and the interpretations of events are represented as links between the states. The ATN that she constructs has twenty-three sates, twenty legal rules, and one hundred generalized `fact patterns.' The latter are associated with each legal predicate, and can be supported by several cases.

The author gives detailed analysis of her approach, and remarks that its use has not produced situations wherein a `tentative' truth value is defeated. Several test problems are analyzed at the end of the book, these dealing mostly with how the reasoning patterns analyze events, and one that deals, interestingly, with legal study aids. From her conclusions it is readily apparent that legal reasoning is very difficult to implement in artificial intelligence, for the primary reason that deduction does not by itself determine the outcome of a case. Another reason is the role of legal precedent, which can give a new interpretation to the language of old rules.

The author also makes commentary on the future of legal artificial intelligence. Considering the progress made in this field since this book was published, especially in the use of knowledge engineering in the practice of law, one can be confident that legal machines will make their presence known in the courts, in legal philosophy, and in constitutional interpretation in the years to come. ... Read more

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This book constitutes the thoroughly refereed post-proceedings of the Second International Workshop on Argumentation in Multi-Agent Systems held in Utrecht, Netherlands in July 2005 as an associated event of AAMAS 2005, the main international conference on autonomous agents and multi-agent systems.

The 10 revised full papers presented together with an invited paper were carefully reviewed and selected from 17 submissions. The papers are organized in topical sections on foundations, negotiation, protocols, deliberation and coalition formation, and consensus formation.

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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.

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This book reports on state-of-the-art research into intelligent systems, models, and architectures for educational computing applications. It provides cross-fertilization of information and ideas from researchers in the many fields that make up this interdisciplinary research area, including computer science, education, educational technology, psychology, and linguistics. The focus is on developing computational models of relevant aspects of learning and teaching processes. One of the central ideas behind Artificial Intelligence in Education, from the very origins of this research area, has been to develop computational learning support systems that maintain a close connection to the development of general cognitive models and architectures. In this sense, an important part of AI-ED is "applied cognitive science". This also implies a certain methodological rigor in the evaluation of intelligently supported learning environments. The subtitle "Shaping the Future of Learning through Intelligent Technologies" indicates a wide range of advanced information and communication technologies and computational methods applied to education and training. Innovation is sought in both the technology and in the educational scenarios. More and more, "design" is seen as a critical element in innovative learning scenarios. Relevant design aspects include interface and interaction design, as well as educational or instructional design. ... Read more

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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.

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The theory of argumentation is a rich, interdisciplinary area of research involving philosophy, communications studies, linguistics, psychology, and logics. Its techniques have found a wide range of applications in both theoretical and practical branches of artificial intelligence and computer science. Multi-agent systems theory has picked up argumentation-inspired approaches and specifically argumentation-theoretic results from many different areas. Researchers in argumentation and multi-agent systems are currently enjoying a unique opportunity to integrate the various understandings of argument into a coherent and core part of the functioning of autonomous computational systems.

This book originates from the First International Workshop on Argumentation in Multi-Agent Systems, ArgMAS 2004, held in New York, NY, USA in July 2004. Besides 12 selected revised full papers taken from the workshop, 4 additional papers by key people in the area round off overall coverage of the relevant topics. The papers address the following main topics: foundations of dialogues, belief revision, persuasion and deliberation, negotiation, and strategic issues.

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The field of Artificial Intelligence in Education has continued to broaden and now includes research and researchers from many areas of technology and social science. This study opens opportunities for the cross-fertilization of information and ideas from researchers in the many fields that make up this interdisciplinary research area, including artificial intelligence, other areas of computer science, cognitive science, education, learning sciences, educational technology, psychology, philosophy, sociology, anthropology, linguistics, and the many domain-specific areas for which Artificial Intelligence in Education systems have been designed and built. An explicit goal is to appeal to those researchers who share the perspective that true progress in learning technology requires both deep insight into technology and also deep insight into learners, learning, and the context of learning. The theme reflects this basic duality.

IOS Press is an international science, technical and medical publisher of high-quality books for academics, scientists, and professionals in all fields.

Some of the areas we publish in:

-Biomedicine
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This cutting-edge resource offers practical overview of cognitive radio - a paradigm for wireless communications in which a network or a wireless node changes its transmission or reception parameters. The alteration of parameters is based on the active monitoring of several factors in the external and internal radio environment. This book offers a detailed description of cognitive radio and its individual parts. Practitioners learn how the basic processing elements and their capabilities are implemented as modular components. Moreover, the book explains how each component can be developed and tested independently, before integration with the rest of the engine. Practitioners discover how cognitive radio uses artificial intelligence to achieve radio optimization. The book also provides an in-depth working example of the developed cognitive engine and an experimental scenario to help engineers understand its performance and behavior. ... Read more

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A must add to any college library collection dedicated to technology
As computers become more and more advanced, artificial intelligence becomes more prominent through technology. "Artificial Intelligence in Wireless Communications" is a technological text focusing on the concept of cognitive radio. A highly technical text, "Artificial Intelligence" explains how to develop AI through radio, teaching it to learn from what it experiences and what one needs to do as an engineer to make all of these concepts a reality. Meant for current researchers and students about to delve into this field, "Artificial Intelligence" provides an excellent reference through and through. Enhanced with appendixes explaining the math, terms, and an index, "Artificial Intelligence in Wireless Communication" is a must add to any college library collection dedicated to technology. ... Read more

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ARTIFICIAL INTELLIGENCE AND SOFTWARE ENGINEERING Understanding the Promise of the Future

The computer is a surprisingly seductive device. It tempts us with the promise of its great power, but also entices the unwary to overstep the bounds of manageable complexity. Managers, business owners, computer literate individuals, and software developers alike are all seeking an understanding of artificial intelligence (AI) and wondering how it might be used.

In this easy-to-read discussion, Derek Partridge helps us understand what AI can and can not do. The topics discussed include:

** strengths and weaknesses of software development and engineering ** the promises and problems of machine learning ** expert systems and success stories ** practical software through artificial intelligence--This text refers to the hardcover edition of this title ... Read more

Customer Reviews (1)

The future isn't what it used to be...
After seeing the 1998 copyright date I confess I was intrigued. AI, in any form, hasn't gotten much press lately. If you're looking for an update on the state of AI since 1990, however, this isn't it.

The book's materials are almost exclusively from 1991 and earlier. Only 3 references are given to sources later than 1991 and two of those sources are from the author himself. That said, the book still has some interesting things to say and some lasting value.

The author's approach is unique: compare standard methods in traditional software engineering to the development approaches necessary for AI work. Partridge spends a great deal of time in the book discussingthe state-of-the-art (in 1990) for software engineering while making occasional comparisons to similar strategies for successful AI application development. As Partridge puts it "in attempting to engineer AI- software we subject the standard procedures of software design and development to close scrutiny--our attempts to build robust and reliable AI-software provides a magnifying glass on the conventional procedures." The author continues this scrutiny throughout the book.

One of the things that makes the book interesting is a view back at what computer science thought AI would have to solve (since traditional engineering practices would fall short). Automatic programming would be needed to help write all these new programs. Having humans do all that would introduce too many defects. Instead, we have "wizards", vast class libraries, and a much stronger set of powerful tools that significantly limit theamount of code that is written. Similarly, the need forreport generators has lessened because the pervasive useof relational databases and the powerful report generation tools.

My favorite was "the problem of decompiling" when discussing reverse engineering. "decompilers are somewhere between scarce and nonexistent..." Consider the modern day UML tools such as Together/J which can take a JAR file (with only code) and reverse engineer an entire UML class hierarchy!

Because the book is not really updated from the early 1990's, there is no mention of genetic programming, no mention of speech software on desktops, and no machine vision advances are discussed, just to name a few shortcomings.

It is an interesting trip down memory lane, and has some interesting things to say about AI and SE and may be worth reading on that front. However, if you want an overview of AI, you will need to look elsewhere. ... Read more