Product Description

Intended both as a text for advanced undergraduates and graduate students, and as a key reference work for AI researchers and developers, Logical Foundations of Artificial Intelligence is a lucid, rigorous, and comprehensive account of the fundamentals of artificial intelligence from the standpoint of logic.

End-of-chapter bibliographic and historical comments provide background and point to other works of interest and research.Each chapter also contains numerous student exercises (with solutions provided in an appendix) to reinforce concepts and challenge the learner.A bibliography and index complete this comprehensive work. ... Read more

Product Description
Artificial intelligence is one of Wall street's most promising new technologies. Used to assist investment decision-making, artificial intelligence systems can handle more information, react more quickly and make more consistent decisions than a group of human experts. In Artificial Intelligence in Finance and Investing, Robert Trippi and Jae Lee thoroughly explain how artificial intelligence systems can help to improve investment returns. Completely updated, this edition also includes sections on neural network and case-based reasoning. Practical and filled with real-life examples, the book provides all the information a financial professional needs to understand and evaluate an artificial intelligence system. For investors who want to stay on the cutting edge of technology, Artificial Intelligence in Finance and Investing will be a must read. Highlights include: overview of artificial intelligence in invesment management; components of an artificial intelligence system; portfolio selection system issues; handling investment uncertainties; practice exercises with K-FOLIO, a typical aritificial intelligence system. ... Read more

Product Description
Written by experts with years of gaming industry experience developing today's most popular games, Game Development Essentials: Game Artificial Intelligence provides an engaging introduction to "real world" game artificial intelligence techniques. With a clear, step-by-step approach, the book begins by covering artificial intelligence techniques that are relevant to the work of today's developers. This technical detail is then expanded through descriptions of how these techniques are actually used in games, as well as the specific issues that arise when using them.With a straightforward writing style, this book offers a guide to game artificial intelligence that is clear, relevant, and updated to reflect the most current technology and trends in the industry. ... Read more

Customer Reviews (1)

Interesting Read
This is one of three books that I purchased of the series. It starts off with an introduction of AI in games and is very well written. The book is structured very well and has nice images thrown in of various games that are being referred to.
As a bonus, the accompanying CD contains some of the code from the book. ... 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
Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that initiated this field of study, presenting the theoretical foundations and exploring applications.In its most familiar form, adaptation is a biological process, whereby organisms evolve by rearranging genetic material to survive in environments confronting them. In this now classic work, Holland presents a mathematical model that allows for the nonlinearity of such complex interactions. He demonstrates the model's universality by applying it to economics, physiological psychology, game theory, and artificial intelligence and then outlines the way in which this approach modifies the traditional views of mathematical genetics.Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways. Along the way he accounts for major effects of coadaptation and coevolution: the emergence of building blocks, or schemata, that are recombined and passed on to succeeding generations to provide, innovations and improvements.John H. Holland is Professor of Psychology and Professor of Electrical Engineering and Computer Science at the University of Michigan. He is also Maxwell Professor at the Santa Fe Institute and is Director of the University of Michigan/Santa Fe Institute Advanced Research Program.Amazon.com Review
John Holland's Adaptation in Natural and ArtificialSystems is one of the classics in the field of complex adaptivesystems. Holland is known as the father of genetic algorithms andclassifier systems and in this tome he describes the theory behindthese algorithms. Drawing on ideas from the fields of biology andeconomics, he shows how computer programs can evolve. The bookcontains mathematical proofs that are accessible only to those withstrong backgrounds in engineering or science. ... Read more

Customer Reviews (4)

Heavily mathematical
Good, however, the Amazon.com listing did not say that this text was geared for Ph.D.'s in Mathematics.

The founder's words
This is a wonderful time. We can read about information theory in Shannon's own words, fuzzy logic in Zadeh's, relativity in Einstein's, and genetic programming in Holland's. He created evolutionary algorithms, and shares his thoughts in this brief work.

1975, when he first published this work, was a long time ago. Since then, computing has advanced, computing demands have advanced, and biology has advanced. Biology, because it functions at all the levels from atoms to worlds, has bottomless potential for insight. Because the atoms, the worlds, and everything between are all unfriendly, biology has many problems to solve. It doesn't matter whether you are an oak tree, a virus, or a whale, the solution (at the species level) is the same: evolve. Holland was the first to harness that incredible problem-solving power to computational use.

A huge literature has built up from Holland's founding thoughts. Those thoughts are here, in their original and purest form. It is hardly surprising that Holland anticipated so many elaborations of his work. One, in particular, struck me: the idea of 'hot spots' for genetic crossover. Or rather the opposite: 'cold spots' where crossover is inhibited. As a computer scientist, Holland's first thoughts were written in binary. When you allow points where crossover can not occur, you allow coherent multibit values - maybe even floating point. It's easy to laugh at Holland's initial naivete now, but he was talking about the foundations, not the structure built up from it.

If you have ever programmed genetic algorithms, you have been stunned by their effectiveness in creating good solutions. 'Good' doesn't mean precisely optimal, but pretty damm good anyway.

If you were a hard core creationist to start with, you still are. But now you know that evolutionary problem solving is powerful, broad, subtle, and effective - so much, that it's hard to believe it could ever have arisen by chance.

//wiredweird

Not an Introductory book
I am learning by myself the topic of Genetic Algorithms (GA) for my PhD dissertation. Even though this book is written for John H. Holland considered the father of Genetics Algorithms, this is not a basic or easy reading book.The book does not contain any source code and even though it contains some kind of pseudocode, it will not give you a clear idea about how to implement a GA. If you want an introduction book maybe you should look for the Mitchell Melanie's book "An Introduction to Genetic Algorithms" , Fogel's book "Evolutionary Computation vol. 1" or Chamber's book "The Practical Handbook of Genetic Algorithms".
The way the author approaches the development of the framework is sometimes overwhelming because the author does not concentrate in one specific case or concept but he mentions all the different possibilities almost at the same time. I think it is worthwhile to buy the book to have it for advanced understanding of the concepts involved in the study of Complex Adaptive System. My approach to learn GA will be reading the above mentioned books and then study this book in a very detailed and slowly way to digest the huge amount of concepts and information provided by it.

Genetic Algorithms Classic for Engineering
This book presents an inspirational synthesis from mathematics, computer science and systems theory addressing genetic algorithms and their role in intelligent engineering/business systems.

Topics include: background, aformal framework, illustrations (genetics, economics, game playing,searches, pattern recognition and statistical inference, control andfunction optimization, and central-nervous system), schemata, the optimalallocation of trials, reproductive plans and genetic operators, therobustness of genetic plans, adaptation of coding and representations, andoverview, interim and prospectus.

Inclusion of a disk ofspreadsheet-based examples would have increased user-friendliness to thesometimes moderately-complex mathematics. Otherwise, this book is a wellpresented, and useful classic for researchers and software vendors seekingto develop more innovative intelligent products. ... 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
This book is a critique of Artificial Intelligence (AI) from the perspective of cognitive science - it seeks to examine what we have learned about human cognition from AI successes and failures. The book's goal is to separate those "AI dreams" that either have been or could be realized from those that are constructed through discourse and are unrealizable. AI research has advanced many areas that are intellectually compelling and holds great promise for advances in science, engineering, and practical systems. After the 1980s, however, the field has often struggled to deliver widely on these promises. This book breaks new ground by analyzing how some of the driving dreams of people practicing AI research become valued contributions, while others devolve into unrealized and unrealizable projects. ... Read more

Customer Reviews (2)

AI is Not What it Seems
Well, at least, that's what Ekbia's position seems to be. He focuses on the fine differences between "true" and "artificial" (if at all) intelligence. This book is not a technical tome and makes for relatively easy reading. However, it would help if the reader was already somewhat familiar with basic AI approaches (in which the book's appendices help). Ekbia discusses computer chess (e.g. Deep Blue), case-based reasoning (e.g. Coach), artificial commonsense (e.g. Cyc), "emotional" robots (e.g. Kismet) and a selection of other examples which provide a good overview of the different perspectives both the public and researchers have about AI accomplishments. Some of Ekbia's arguments are difficult to argue against. For example, it is true that some researchers overstate their case in terms of just how "intelligent" or significant a particular approach is. Computers, after all, work in a mechanical fashion and have no real conception about the things they are working with. In many cases, this is obvious once you look "under the hood"; but in others, it is possibly just a matter of perspective. Take chess, for example. While the brute-force approach seems to work well and is purely mechanical, we cannot overlook the significance of the heuristic evaluation functions which are equally important. These are usually specifically designed by humans. Not to mention that this combination has resulted in programs running on desktop machines that can today outplay even grandmasters. In fairness, Ekbia does not trivialize this type of "success" in AI but suggests, rightfully, that we have perhaps just found a different approach to "thinking" in chess, and chess alone. But this is how it is in AI. The approach used in chess is neither required nor expected (at least these days) to be directly applicable to other areas with equal effectiveness. This, however, does not mean that at least some of the work done in that domain has not been useful in other domains or fields of research.

Most of the book, including some gems in the footnotes at the back, hover around the point that we are somehow missing something in AI that would put us on the "right path", and that we are, at least, approaching this path slowly, perhaps without even realizing it. With a rich and colorful history behind AI, its future is unlikely to suffer from exactly the same mistakes despite the necessary evil or growing temptation faced by researchers to somewhat mislead industry-related benefactors into thinking they are financing something truly significant. I found myself generally sobering up to Ekbia's insights into AI and learning of happenings in the field that I was previously unaware of myself. Many books on AI will likely come off as highly technical and complicated (a lot of math is usually involved) but this one takes a "higher level" or philosophical approach which, I now think, should not be neglected even in undergraduate study of the field. One should, however, be careful not to give undue reverence to the idea of simply "being human" just because of the current shortcomings in AI. I am nevertheless certainly glad I made it a point to read the book while waiting for my viva voce.

Falls short in its criticism
If one referred to program lists or algorithm steps as a "reasoning pattern" and the program itself as a "cognitive structure" the author of this book would be taken aghast, and might take this as another example of what he refers to as the "generalized Eliza effect" throughout the book. The latter refers to a program, called by its creator "Eliza" that was designed to emulate the psychotherapeutic skills of a professional psychologist. Developed in the 1960's by Joseph Weizenbaum and tested on a collection of unsuspecting "patients", it apparently was able to fool some of these into believing its advice was genuine, or expert psychological counseling.The "patients" questions were recast in such a way as to create the illusion that the program had genuine understanding of psychotherapy and could offer them therapeutic assistance. But the Eliza responses were merely canned phrases, as was revealed by the patient "patient" who took the time to question it for several minutes.

The author uses this program as a paradigm for his main case against the reality of machine intelligence, viewing the program as an excellent example of the false imputation of intelligence to a machine. He gives many other examples throughout the book, all of them being quite familiar to those readers who follow the field of artificial intelligence (AI) or who are active participants in research thereof. As a whole the book is interesting, mostly due to the detail that the author brings to the history of AI and the discussions of some of the attempts to bring about machine intelligence.

However the author's case against AI is incredibly weak, being non-constructive in its strategy and actually being one of many critiques of AI that fall victim to what this reviewer has dubbed the "Michie-McCorduck-Boden effect." This effect, kind of an inverse of the Eliza effect, summarizes the peculiarities and crises of confidence that have dogged research in AI since its inception in the early 1950's. The following quotation from the writer Brian R. Gaines encapsulates it beautifully:

"From the earliest days of AI pioneers such as Donald Michie have noted that an intrinsic feature of the field is that problems are posed such that all those involved accept that any solution must involve `artificial intelligence' but, when the solution is developed and the basis for it is clear, the resultant technology is assimilated into standard information processing and no longer regarded as `intelligent' in any deep sense. When the magician shows you how the trick was done the `magic' vanishes. Much of what has been developed through AI research has diffused in this way into routine information technology: the Michie effect."

Other authors, AI historians, and researchers have made similar commentary as to the nature and progress in the field of AI. In particular the AI historian Pamela McCorduck and the cognitive scientist Margaret Boden have discussed this phenomenon at length. It could thus be referred to as the Michie-McCorduck-Boden effect, and it has huge consequences for general acceptance of machine intelligence, especially for specific views of whether or not a machine is exhibiting intelligence.

The Michie-McCorduck-Boden effect can be considered an inverse "Eliza" effect (as the author describes the latter) in that those who fall under its spell are quick to impute non-intelligence to machines as soon as they uncover "the method behind the magic." The author does this several times in this book: in his criticism of connectionism, the Cyc project, and Deep Blue. Once he discovers the processes or algorithms that each of these "programs" uses, he points out their shortcomings in semantics and a notion of "meaning" that he never really explains to the reader. But he still wants to describe human cognition as "intelligent", which he refers to as a "complex, multifaceted, and multilevel phenomenon."

But is human cognition the way he describes it? And once it is "unraveled" as he puts it, will it in turn be delegated to a trivial collection of processes in the same way as chess programs and natural language processors (e.g. Cyc) have been? If historical trends are to be followed with respect to the science of human cognition as they were in research in AI, there is every reason to believe that once the "method behind the magic" of human cognition is discovered it will trivialized in just the way that machine processes are. Will the notion of "intelligence" then fade from scientific discourse, both in machines and humans? Maybe.

The book is thus full of examples of projects that fall short if judged from true intelligence or "meaningful" knowledge as the author discusses it (and he does so with the admission that the dividing line between information and knowledge is too "fuzzy"). But what is so deeply troubling is not the vagueness in which the author addresses these issues, but rather the insistence that the AI projects such as Cyc and case-based reasoning must be complete or all-encompassing before we can regard them as intelligent. If Cyc gets bogged down in a question-answer session for a particular domain it must rejected the author seems to argue. He forgets that even human experts in particular domains, like physics for example, typically make mistakes in their scientific narratives, and we certainly don't want to reject their expertise outright because of a few blunders on their part. A reasonable outlook on the projects discussed in the book would consist of estimating the risk that the user takes on when using machines that deploy Cyc, case-based reasoning, or connectionism. Such machines will not be "fool-proof and incapable of error" and their solutions to problems or answers to questions may seem foolish or incomplete at times. But such is the nature of intelligence, and insisting otherwise puts unreasonable expectations on machines (or humans for that matter).

This reviewer therefore disagrees strongly with the author's conclusions, but does agree that one must emphasize both the practical applications of AI as well as the theories and formal constructions behind these applications. One must also step beyond the media and advertising hype, the overindulgences of Hollywood movies and rapid-fire press releases, and give an honest and objective assessment of the status of AI as it exists at the present time. Therefore "futurism", unjustified optimism, and wishful thinking should be carefully guarded against. On the other hand great care should be taken to distinguish skepticism from cynicism, and there should be no hesitation from expressing emotion when contemplating genuine discoveries in machine intelligence. We must not be guarded in our enthusiasm in this regard.

The field of artificial intelligence is a healthy one, and delivers practical technology, with its influence rapidly increasing in the twenty-first century. For better or worse, and in spite of the tremendous social changes that AI might cause, from a rigorous and careful study of the evidence, we can turn Hollywood on its head and use one of its movie titles with pleasure: we can say with confidence that we are entering a world of the silicon geniuses; a world of the avatars. We are witnessing the rise of the machines. ... Read more

Product Description
Even as molecular biologists attempt to reproduce life in vitro, another group of scientists is creating life--or something very close to it--in silico, using computers to produce "organisms" that can move, see, feed, reproduce, and die. Photos. ... Read more

Customer Reviews (14)

Superb!!
AL is popular science writing of the first order: informative, clear, fascinating, and entertaining.My only disappointment is that it was published in 1992, and thus does not touch on developments in the field since then.I'd love to know how these have panned out, and whether scientists remain enthusiastic about the possibilities of A-Life.Judging from the textbooks on A-life that have been published since 1992, the field is alive, at least, and I can only assume it is well to boot.I'll have to hunt for bibliography elsewhere.My thanks to Levy for sending me on this hunt.AL is a book to fire the imagination.I'd give it 10 stars!

A note on the metaphysical material in AL that bears on the question of whether present iterations of 'artificial life' are, or whether future iterations may one day be, sufficiently complex that they should be considered true LIFE: throughout, Levy stresses the essential link between an (')organism(') (wet or dry) and its environment.Yet, it seems to me, in discussing the question of the LIFE-status of in-silico 'organisms', he considers the 'organisms' alone.I wonder whether this apparent preference reflects his own bias, or a bias on the part of the scientists he profiles?From the perspective of emergent behavior and the capacity to evolve, etc., AL 'creatures' self-evidently bear a striking resemblance to biological creatures.It strikes me, however, that a key consideration in the wet-life as LIFE versus dry-'life' as LIFE argument -- is that wet-life organisms express emergent behavior and evolve, etc., in environments that are, throughout, rife with other life, whereas dry-'life' 'organisms' do the same in environments that are otherwise sterile (by the standards that A-Life scientists themselves would apply).Some consideration of how environments contribute to the LIFE-status of particular (')organism(')s, and of any definition of LIFE (wet or dry) itself, seems to be of the essence.Yet another thought to pursue -- though doubtless ethologists, philosophers, and A-Life scientists have beaten me there.Proof positive that AL is a highly thought-provoking book.Read it!

Great Beginners book
I just loved this book. It gives the novice a very good sampling of the future of Artificial Intellegence and Artificial Life. I particularly enjoyed the chapter on the discovery of machine virus'. Somewhat dated, but an extremely good read.

My Review of this Book
I have read this book.

It is about artifical intelligence. If you have a computer you will know exactly what I mean. When you hook up a computer, it acts alive, and you gotta interact with it like it is artifically intelligent.

Like when I hook up the voice-recognition thing where you speake into the mikerofone, it acts like it hears you too, and does what it is told to do. Sometimes that is to write a letter, or to tell it to go onto the net.

I told my computer to go onto the net once thru the mike, and it did it, as it was spoken and said what to do.

So if you read and buy this book you will learn to do this, and hook it up yourself. The book has plans and charts to do all this stuff. When you read it, pass it onto a friend, and they may help you once they read it themselves.

I gave this book 5-stars, because it was a very good one, and I will now know how my computer is so smart. I told it what to do, and it help me with this revue to. So buy it but just one time, because a friend and other people will be able to read this for free, once you give it to them.

Engines are my hobbie, and so are electronic power supplys, so I plan to use this book for that to. I will design new ones that are faster than sound, and my computer will be smart and help me with that.

So buy this book, once, and you will like it along with all the friendly people that you knowe.That's my revuiew, but I will do anew one when a new adition of the book comes out to the press.

I do recomend that you buy this one time for the people who wanto know about how artifical intelligent computers get smarter and help you with life-things you need to do, but not all by yourselfe, but with a computer.

An excellent intro to a new science
While the concept of artificial life has been around at least since humans developed self-awareness, the commensurate decline of religion and rise of the scientific method was necessary for it to become a point of real debate.However, it was not until September 1987 when the event occurred that established a-life as an academic discipline, namely a conference devoted to its study.This work uses that event as a starting point, and does a superb job of presenting nearly all perspectives, including historical.
Like its counterpart, artificial intelligence, the discipline of a-life suffers from a lack of definition. There is no agreement on what life or intelligence are.Additional disagreement arises over the following distinctive descriptions of life.

(a) Objects such as rocks can be assigned a life (intelligence) value of zero and as we moveupward to humans and beyond, the measure of life (intelligence) characteristics isdescribed by a smooth, continuous function where the first derivative never becomes very large, but is always positive.There is no clearly discernible boundary between life and non-life.

(b) Starting from the same initial position as (a), the derivative stays close to zero for some time, and then suddenly becomes unbounded, as the matter now possesses the fundamental essence of life (intelligence).That point of the vertical derivative is the boundary point between animate and inanimate objects.

Much of this book deals with cellular automata and the algorithms used to create them.Like so many new, perhaps revolutionary disciplines, the major players tend to be free spirits.Many of the people described here bounced around before finding their ecological niche in a-life.With the exception of the originators, John von Neumann and John Horton Conway, those who established the study of cellular automata as an academic discipline were academic outsiders who literally created it from nothing.The explanation of that is very well done.While most of the work has been done by computer, no previous knowledge is necessary to understand the text.
One item could have been better handled, but that is largely due to the problems with definitions.Like the workers in chaos, a-lifers tend to see what they want to see.For example, simple rules are used to create an image that either looks or acts like something known to be alive and this is used to argue that life is being created or that the rules that create life are simple.Which is an extremely weak argument.What is being created are items that human eyes interpret as looking like life, and as all psychologists know, the human brain processes images with a bias towards previous experience.The devil's advocate against is a shadow here.However, it is difficult to argue in the negative when you are aiming at a nebulous target.
Whatever your interest in a-life, you will find something of value in this book.Biologists and philosophers who teach general education courses will also find a good deal of discussion material.The hypothetical qualification has been removed form the debate, as there are now objects to argue about.

Published in Journal of Recreational Mathematics, reprinted with permission

fascinating
I read this more than three years ago, before I started my undergraduate studies. I knew I was going to study computer science, but after reading this book I knew I would forever be drawn to the multidisciplinary fields of biology and computer science. From the question of the origin of life to intelligence, the book convinced me that a new approach is needed to solve these old mysteries.

It's not a masterpiece of literature, but it was interesting enough to forever change my research career. ... Read more

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This book provides a rigorous look at the mechanisms underlying collective behavior in social insects. The field is developing rapidly, and the book includes up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics. ... Read more

Customer Reviews (3)

Impressively good, but not an introduction
Compared to "Swarm intelligence" by James Kennedy, this one is not introductive but gets quite deep into the working of applying the "swarm" paradigm to optimization problems. I would rather recommmend for a person not used to meta-heuristics and optimization to first go to the book by Kennedy. Only if one is interested in using swarm for solving real optimization problems reading this one is a good idea.

This book illustrates several features of swarm behavior that can be leveraged for optimization. The authors writing style is equivalent to technical papers, so be prepared...this is no easy book.

A first milestone in the study of Swarm Intelligence
The book of Bonabeau, Dorigo, and Theraulaz is an excellent example of synergetic work between a physicist, an engineer, and a biologist. The Swarm Intelligence principles are first described and understood through models in natural systems and then translated in optimization algorithms,distributed algorithms for robotic control, and so on. Even if the bookdoes not completely succeed in linking all three disciplines together -computer science, engineering, and biology - under a sound, commonformalism, it represents an extremely up to date collection of work carriedout worldwide in the field of Swarm Intelligence. I strongly believe in thefuture of this field and of its applications to problems hard to tacklewith classical techniques. This book summarizes in an very equilibrated waythe early, promising steps of Swarm Intelligence.

Algorithms inspired by social insects
A good synthesis of studies on swarm intelligence. It is fascinating to see how complex intelligent behavior can emerge from simple rules and numerous interactions without any plan or centralized coordination.Algorithms inspired by social insects can be applied in many disciplines.It is a book easy to understand but difficult to read through for those whodon't love algorithms. It includes a very neat introduction to the subjectwith many clear examples. Everyone should read that part and at least throwa glance at the rest of the book. ... Read more

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In the 1950s, a new field, cognitive psychology, emerged as a dialogue between the growing capabilities of digital computers and the study of human cognition and perception. Artificial Intelligence (AI) researchers began to develop models of perception, reasoning, knowledge organization, and natural language communication. They also created neural networks, expert systems, and other software with practical applications. AI models in turn have offered provocative insights into the human mind; now, new developments in virtual community and cyberspace point toward a future in which human and computer minds will interact in increasingly complex ways. Ultimately, AI research compels us to ask what it is that makes us human. "Artificial Intelligence" presents dynamic new portraits of the men and women in the vanguard of this innovative field.Subjects include Alan Turing, who made the connection between mathematical reasoning and computer operations; Alan Newell and Herbert Simon, who created a program that could reason like a human being; Pattie Maes, who developed computerized agents to help people with research and shopping; and Ray Kurzweil, who, besides inventing the flatbed scanner and a reading machine for the blind, has explored relationships between people and computers that may exceed human intelligence. ... Read more

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Good overview of GOFAI
This is the first volume of a 3-volume set published in the early 1980's and thus could be thought of as a summary of what was known at the time in the field of artificial intelligence (A.I.). Now sometimes referred to as "GOFAI" for "good ole-fashioned artificial intelligence", this set of books can still be referred to profitably by anyone curious about the applications of artificial intelligence. Indeed, many of the algorithms discussed in this volume are still being used, and very robustly, in current implementations of artificial intelligence. A lot has happened since this volume was published, especially in the area of chess playing and logic programming, but there are many sections of the book that are still up-to-date.

After a brief introduction to A.I. in chapter one, chapter two overviews the use of search algorithms for intelligent problem solving. The emphasis initially is on the problem representations that form the basis of search techniques, such as state-space and problem-reduction representations. Game tree representations are also discussed. The algorithms that implement the problem representations are then treated. If the search space is viewed merely syntactically, these are called "blind search" algorithms, which are distinguished from "heuristic" methods, which exploit various structural information about the problem in order to limit the search. Examples of blind search methods that are discussed include breadth-first, uniform-cost, depth-first, and bidirectional search. Examples of heuristic methods discussed are ordered state-space, bidirectional, and the famous A*-algorithm, the latter of which is still finding considerable use in new applications of A.I. Examples of game tree search that are covered include the minimax procedure, the negmax formalism, and alpha-beta pruning. There is discussion on the use of heuristics in game tree search, but this part is out-of-date due to the advances made in chess playing, checkers, etc, since this volume was published.

Chapter three is an overview of knowledge representation in A.I. The author takes a pragmatic approach to the nature of knowledge and intelligence, and defines the "representation of knowledge" as a combination of data structures and interpretive procedures that will lead to what he calls "knowledgeable" behavior. A book needs a reader before it could be considered knowledge, argues the author. He calls this whole enterprise "experimental epistemology" , which endeavors to create programs that exhibit intelligent behavior. The chapter gives an overview of the knowledge representation schemes used in A.I. and discusses their uses and shortcomings. Also, the tension between the advocates of declarative versus procedural knowledge representations is discussed. Declarative systems are more logical/mathematically based, and were exemplified by theorem-provers based on logical resolution. The procedural approach emphasized a more directed approach to the problem of inference and one that makes the reasoning process more understandable.There is a brief discussion on semantic nets, which were invented as a model of human associative memory. The net consists of nodes, which represent concepts, objects, or events, and links between the nodes. The relevant facts about a concept can be inferred from the nodes to which they are linked directly, and so an extensive database search is not necessary. The semantics of net structures depends only on the program that uses them, and so any notion of "logical validity" of inferences from using the net is absent. Production systems are also discussed in this chapter, these being developed as models of human cognition. These systems are called "modular" knowledge representation schemes in that the database consists of rules, or "productions", that take the form of condition-action pairs. The conditions in which each rule is applicable are made explicit and thus the interactions between the rules are minimized. These systems have been used to control the interaction between declarative and procedural statements and to develop autonomous learning systems. In addition, the chapter includes a discussion of the "frame" knowledge representation system, which at the time of publication, was just getting started in A.I. research. It has been widely discussed since then, mostly in the context of studying how to implement reasoning about actions, and became to be known as the "frame problem". The proliferation of the frame axioms needed made reasoning about actions difficult or cumbersome, but was later solved using what are now called "successor-state axioms". The chapter also includes a discussion of the standard logical representational schemes: propositional and first-order predicate logic. Since the time of publication, and due to the interest in developing "common-sense" reasoning machines, second-order predicate logic has made its appearance in A.I. research, sometimes being called "ontological engineering" in the literature. Also, due to the time of publication, there is no discussion of inductive logic programming, which has recently gained importance in A.I. research and its applications.

Chapter four covers the very important topic of natural language understanding. This is one of the areas in A.I. that has been the target of an enormous amount of research, for the ability of a computer to converse with a human fluently and with understanding would be a major advance for A.I., perhaps even an "acid test" that true intelligence has finally been achieved in a machine. The chapter gives a brief history of research in natural language processing and discusses the early attempts at machine translation from one language to another. There is also extensive discussion on grammars, parsing techniques, and text generation. Several examples of programs used for natural language processing that were popular at the time of publication are discussed. ... Read more

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Computer Science ... Read more

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A Classic in the field and Still Inspiring
This book is one of the most enjoyable and inspiring books that I have read, especially if you are interested in Natural Language Processing and Literary Analysis.Many of the authors' original ideas and researchcontributions are still worthy and valid for today's research topic anddirections, such as in data/knowledge mining, intellegent abstract &indexing, etc.. ... Read more

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Written by 350 experts in industry, government and academia, this award-winning book covers all fields encompassed by Artificial Intelligence. The Second Edition has been expanded and updated to include growth in the areas of fuzzy logic, vision, neural networks and languages. Contains over 50% new and revised articles, more than 5000 literary references and 450 illustrations. The excellent indexing and cross-referencing system will lead readers to almost every other article. ... Read more

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Encyclopedia of Artificial Intelligence
Concepts and definitions are comprehensive and informative , filled with scholar works and yet without munbo-jumbo jargons which often throw interested readers. Great resources on the AI subjects.

useful.
If you are new to AI or even a have some history in AI research, this bookwill serve you well. You will not only find the latest information in AIand related fields, but you can consider the book as a root for all yourresearch; it offers a rich references listings in all fields of AI andmore. If the version is new, don't bother browsing for references, StartHere.

excelent
goo ... Read more

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The book presents some of the most relevant results from academia in the area of Artificial Intelligence for games. It emphasizes well theoretically supported work supported by developed prototypes, which should lead into integration of academic AI techniques into current electronic entertainment games.The book elaborates on the main results produced in Academia within the last 10 years regarding all aspects of Artificial Intelligence for games, including pathfinding, decision making, and learning. A general theme of the book is the coverage of techniques for facilitating the construction of flexible not pre-scripted AI for agents in games. Regarding pathfinding, the book includes new techniques for implementing real-time search methods that improve the results obtained through A*, as well as techniques for learning pathfinding behavior by observing actual players.Regarding decision making, the book describes new techniques for authoring tools that facilitate the construction by game designers (typically non-programmers) of behavior controlling software, by reusing patterns or actual cases of past behavior. Additionally, the book will cover a number of approaches proposed for extending the essentially pre-scripted nature of current commercial videogames AI into a more interactive form of narrative, where the story emerges from the interaction with the player. Some of those approaches rely on a layered architecture for the character AI, including beliefs, intentions and emotions, taking ideas from research on agent systems.The book also includes chapters on techniques for automatically or semi-automatically learning complex behavior from recorded traces of human or automatic players using different combinations of reinforcement learning, case-based reasoning, neural networks and genetic algorithms. ... Read more

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Computational Intelligence: Principles, Techniques and Applications presents both theories and applications of computational intelligence in a clear, precise and highly comprehensive style. The textbook addresses the fundamental aspects of fuzzy sets and logic, neural networks, evolutionary computing and belief networks. The application areas include fuzzy databases, fuzzy control, image understanding, expert systems, object recognition, criminal investigation, telecommunication networks, and intelligent robots. The book contains many numerical examples and homework problems with sufficient hints so that the students can solve them on their own. A CD-ROM containing the simulations is supplied with the book, to enable interested readers to develop their own application programs with the supplied C/ C++ toolbox.

Customer Reviews (1)

Computational Intelligence: Principles, Techniques and Applications
Excellent book. I highly recommend it. Although Computational Intelligence could include almost any subject, this book is a comprehensive review of the most agreed-upon paradigms in CI. Haven't checked out the CD that comes with the book, so I cannot comment about it. ... Read more

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Mathematical Methods in Artificial Intelligence introduces the student to the important mathematical foundations and tools in AI and describes their applications to the design of AI algorithms. This useful text presents an introductory AI course based on the most important mathematics and its applications. It focuses on important topics that are proven useful in AI and involve the most broadly applicable mathematics.

The book explores AI from three different viewpoints: goals, methods or tools, and achievements and failures. Its goals of reasoning, planning, learning, or language understanding and use are centered around the expert system idea. The tools of AI are presented in terms of what can be incorporated in the data structures. The book looks into the concepts and tools of limited structure, mathematical logic, logic-like representation, numerical information, and nonsymbolic structures.

The text emphasizes the main mathematical tools for representing and manipulating knowledge symbolically. These are various forms of logic for qualitative knowledge, and probability and related concepts for quantitative knowledge. The main tools for manipulating knowledge nonsymbolically, as neural nets, are optimization methods and statistics. This material is covered in the text by topics such as trees and search, classical mathematical logic, and uncertainty and reasoning. A solutions diskette is available, please call for more information. ... Read more

Customer Reviews (4)

Good, but somewhat outdated
This is a good introductory text in the mathematical backgound of AI. It covers the problems of searches, logic programming, different types of reasoning, neural networks as well as a little bit of probabilities.

Its great merit consists in the fact that it is not disconnected from the realities of the world. The chapters in Prolog, for instance, are well developed and the mathematical foundation of this programming language is quite thoroughly explained. This is rare to find in Prolog or logic books; most of them are either too pragmatic or too theoretical. This book makes a nice balance between the two.

The book has some drawbacks, though. First and foremost, it is geared a little bit to much on logic at the expense of other intelligent forms of computing (pattern recognition - be it vision, speech or handwriting, planning, constraints processing, theorem proving, case-based reasoning, to name just a few).

For example, the section dedicated to stochastic processing is ridiculously small.

However, as a good introduction into the math of AI, this book lives well up to expectations.

Interesting but content bit disconnected
Most topics are interesting and contribute to an understanding of AI.Only point of confusion is some sections seem more like authors personal issue rather than a connected discussion of AI.Expected more because of the many recommendations for the authors work.

Excellent
Although using only elementary mathematics, and not at all addressing new areas of artificial intelligence, such as inductive logic programming, this book gives an excellent overview of how mathematics is used in artificial intelligence. Mathematics at all levels is used in this field, both in the algorithms and in discussing its foundations, and this book serves as a good introduction to its application in A.I. Only elementary algebra and calculus are used in the book, making it very accessible to the beginning student in computer science. Readers with more sophisticated background in mathematics can then extend the results in the book to more advanced mathematical contexts. The author's writing style is very informal, and in many places in the book he encourages the reader to "stop and think" before continuing in the reading. Exercises, some simple and some very challenging, are found at the end of most chapter sections.

The author gives a brief overview of the history of A.I. in chapter one, including a discussion of the issues of computational complexity in A.I. algorithms, a discussion of expert systems (with examples), and a few biographical sketches.

Chapter 2 is a fairly detailed overview of search algorithms, and the author introduces some notions from the mathematical field of combinatorics, namely directed graphs and ordered trees. Induction and recursion are then reviewed as tools for search algorithms. The recursive formulation of algorithms in A.I. is of course very powerful, and one that students need to master early on. Fields such as bioinformatics and data mining are becoming increasingly dependent on search algorithms from A.I., and the author reviews these in detail, including 'simple' search methods such as breadth-first, depth-first, and iterative-deepening, along with 'heuristic' methods.

The reader gets introduced to first-order predicate calculus in chapter 3. This topic could be said to be one of the most important ones in A.I., and it is discussed in this chapter using the (declarative) programming language Prolog. One could easily use the language Lisp, but Prolog makes more apparent the head/body clause structure of predicate logic. In addition, if a reader wants to move on to more modern developments in A.I., such as inductive logic programming, which can be viewed essentially as predicate logic but with inductive reasoning, a mastery of the content of this chapter is essential.

Chapter 4 introduces the reader to the proof theory, namely the technique of resolution, which is discussed for propositional calculus, where it is very simple, and for predicate logic, in the latter wherein some specialized techniques must be brought in, such as Skolemization. The author also discussed proof in the context of Prolog, and introduces the cut operator, which inhibits Prolog from fully implementing resolution. He also gives an interesting discussion on the problem of negation in Prolog and the closed-world assumption.

The author has been careful to not write a purely theoretical book in computer science, and evidence of this is given in chapter 5, which discusses how to implement first-order logic (FOL) into real-world applications. It is one thing to discuss the properties of logic, quite another to actually use it productively to solve problems of interest. The author discusses the limitations of FOL in these applications, and how they can be resolved through alternative reasoning tools, such as nonmonotonic logics, Bayesian networks, and fuzzy sets.

One of these alternatives, nonmonotonic reasoning, is discussed in the next chapter, wherein the author gives a fairly detailed overview of default reasoning and how it is implemented in Prolog. Rule sets and semantic nets are also discussed, along with defeasible reasoning. Applications of these techniques are stymied by their computational complexity, and the author gives references for discussions of this.

After a review of probability theory in chapter 7, the author discusses Bayesian networks in chapter 8. These have been extremely important in recent applications of A.I., and the author gives a fine review of their properties, especially their ability to incorporate causality by imposing a directed graph structure on the event space. The author gives a few examples of Bayesian networks, including a medical diagnosis, wherein he introduces a very important concept in A.I., namely that of abductive inference. Detailed discussion (with proofs) is given for the Kim-Pearl algorithm for singly connected networks.

Chapter 9 is an introduction to fuzzy logic and belief theory. The author motivates nicely the reasons for considering fuzzy reasoning instead of probabilistic methods. The Dempster-Shafer belief theory, which has become popular in recent years, is also discussed in some detail.

So as to motivate the discussion of neural networks, the next chapter overviews automatic pattern classification. Contrasting between supervised and unsupervised learning of patterns, the author then outlines the types of automatic classifiers, such as decision trees and neural networks. The chapter on neural networks is a good introduction considering the vastness of the subject. Indeed, an enormous amount of research has been done on neural networks, and their use in applications of A.I. has finally been achieving success in recent years.

Concepts from information theory are of course very important in A.I. and these are discussed in chapter 12, along with more advanced topics in probability and statistics that were not treated earlier in the book. These ideas are used in the next chapter wherein neural networks and decisions trees are discussed in more detail. The most interesting part of this discussion is the idea that noise can improve the generalization capabilities of neural networks. This strategy will be obvious to the physicist reader who has studied the effects of noise on dynamical systems governed by potentials with local minima.

The last chapter of the book discusses some additional topics that should be included in a study of A.I., such as genetic algorithms and more discussion of optimization, such as simulated annealing. Hidden Markov models are also briefly discussed, and this is somewhat disappointing given their importance in current applications. The reader is also introduced to robotics, certainly the most exciting of all topics in 21st century A.I.

It is a useful book for research oriented readers.
Most AI books do not emphasize the mathematical issues. Consequently, the readers face difficulty to read journals. This is a highly recommended book for those research oriented readers. It requires no formal background ofmathematics beyond high school level. I read the book several times. Ithelped me a lot to understand many difficult papers. Among the chapters themost useful are chapter 6 on nonmonotonic reasoning and chapter 8 onBayesian networks. The beginners will find chapter 3 and 4 on predicatelogic and the theory of resolution highly useful. I strongly feel that thebook should be read by all people working in the domain of AI. ... Read more

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Based on materials discussed in the various quantum probability conferences, this text aims to provide an update on the rapidly growing field of classical probability, quantum physics and functional analysis. In this book, a pioneer in the research on collective learning systems (an adaptive learning paradigm for artificial intelligence) describes the processes and mechanisms of human and artificial cognition, defines a fundamental building block for assembling large-scale adaptive systems (the learning cell), and proposes a design for the ultimate: a hierarchical network of 100 million learning cells that exhibits the full range of cognitive capabilities of the human cerebral cortex. The author demonstrates that using the classical "expert system" approach to create such a vast knowledge base would require thousands of years to program all the necessary rules. He then explains how an adaptive collective learning system could achieve this goal in a matter of 20 years, much as humans do. Based on natural anatomical and behavioral precedents, collective learning enables a machine to learn the appropriate rules through trial-and-error interaction with the real world.In the course of explaining the principles of collective learning and his design for the ultimate machine, the author introduces a new theory of games for modelling the processes of the universe and discusses the philosophical issues raised by the prospect of creating machines that exhibit human-like intelligence. In addition to a number of small-scale illustrations of Collective Learning, the final chapter presents the remarkable results of a research project directed by the author: a simulatin of the sub-symbolic image-processing functions of the primary visual cortex of the brain. ... Read more

Customer Reviews (2)

Collective Learning explained
I have been interested in AI for a very long time. After reading this book I was enthralled to try some collective learning programming of my own. The book gave a very solid description of the algorithms involved, and was enough to allow me to build my own simple collective learning algorithms. Anyone intereted in learning or neural networks should buy this book.

The definitive work on Collective Learning Systems.
This is a must read for anyone serious about Machine Learning and Cognition! Peter Bock, an internationally recognized scientist, presents his theories and associated technology for the coming generations ofadaptive intelligent machines. He discusses the processes of cognition,postulates a fundamental adaptive building block for assembling verylarge-scale collective learning systems, and proposes a design for amachine that could exhibit the full range of cognitive capabilities of thehuman mind. The book includes discussion of game theory, artificialcognition, and the philosophical issues raised by the prospect of creatingmachines that exhibit human-like cognition. The book is an easy andentertaining read. I highly recomend it. ... Read more