Artificial Intelligence Books

Mathematically sophisticated and quite original. The Finnish simulation, which accurately models language change, is particularly impresssive.

As a student who actually studied the theory of Analogical Modeling of Language under the author, Skousen, I found this book to be a fascinating and brilliant insight into linguistic analysis. Obviously, some study of language or linguistics is a helpful background for this book, but it is a straightforward explanation of Skousen's theories with ample evidence to back up his claims.

What is fascinating about Skousen's work is that it addresses the fundamental questions that Noam Chomsky often avoided in his theory of transformational grammar--how is it that we are able to use so many rules in language without ever having to go to school for them? Skousen's model basically addresses how the human brain is able to process language through "analogy"--in other words, Skousen's major supposition is that people learn to create language by creating datasets in their heads. As they get older, the datasets get larger. This may explain why certain children use tenses wrong (I thunk, I knowed it). Skousen's book also demonstrates exactly how to build a dataset and empirically research certain linguistic phenomenon. He uses one example, the a-an frequency in English. He demonstrates that using the article "a", as often as we do, shows an amazing amount of "leakage" toward making mistakes away from an. So a child with a smaller data set would end up making mistakes like "a apple", simply because the child's mind is so overcome with "a" examples that movement toward the "a" becomes possible. This textbook is a must for those interested in artificial intelligence, real language use, and linguistics. Shane Dixon (sydixon@hotmail.com)

This book is so confused about what it wants to say, it's like being trapped in a car with a neurotic babbler on a long roadtrip to nowhere. Furthermore, it's overly impressed with itself. Frankly, this book accomplished something that is rare in my readings: it left me less impressed with the subject than when I started out. After reading this, and rooting around for other literature, and projects, CBR, though it is based on a solid premise (from Philosophy, not science ;) ), is just a rhizome waiting for some soil and water.

This book is easily the best of its type, there is no other introductory book on CBR available! I bought this at AAAI-97 and found it extremely easy to read and sensibly layed out. It provides a good comparison of CBR with other AI and IS techniques and clearly explains what CBR's unique strengths (and to be fair) weaknesses are. A sensible range of application case-studies both academic and commercial are described and a wide range of CBR tools are described and compared. The book concludes with some methodological guidelines for building case-based systems. I would have no hesitation in recommending this book to undergraduate and postgraduate students who want an approachable introduction to CBR. The book will also be invaluable to commercial developers who are thinking of deploying a case-based system. Finally, the book's remarkably extensive bibliography, which is categorised to make finding references on specific subjects easy to find, will mean that ALL researchers in CBR should buy this book - it will save you days in the library. To conclude, an excellent book that will satisfy a wide range of readers

The author gives a comprehensible introduction to the field of Case Base Reasoning (CBR). He starts with an overview to the various techniques and algorithms to be used with CBR and formulates practical criteria when to use them and when not (e.g. adaptation). Very usefull are the differences to other AI approaches and the criteria to check the applicability of these methods. A chapter on CBR software tools reviews a series of available tools and development shells. Practical examples, especially on helpdesk applications show how CBR has been used successfully in projects. All in all the book demystifies CBR and encourages the use of it for building up decision support systems.

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

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

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

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

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

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

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

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

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

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

This is a great exposition of the fundamental notions involved in the philosophy of AI. While at first look may appear like a good undergraduate read, it is, in fact, quite subtle and deep in most of the material it touches. Great scholarship.

The heart of this book is the step-by-step instructions to construct seven different Lego robot chassis. This is just like any Lego instructions you will find, but it's not in color, and there is English text to add more material about the reasons behind the design.

In addition there is Mindstorms control code to accompany each chassis design. The programming language is Not Quite C (NQC). Installation instructions for NQC are included as this is a step you will have to take in addition to the standard Mindstorms installation.

The bots range from just a small construction around the RCX control block to designs that are 'gargantuan'. They are as sturdy and well designed as those that you find in the original Mindstorms kit.

I was disappointed that there was not more emphasis placed on describing the program logic. I would have appreciated flow charts as most of the control logic is simple state machines.

Overall I think this would be a fine book for anyone serious about Mindstorms sumo.

Competitive Mindstorms: A Complete Guide To Robotic Sumo Using Lego Mindstorms is a hobby guide to using the Lego Mindstorms Robotic Invention System to design, build, program, and unleash autonomous "sumo-bot" creations for use in a robotic sumo arena. Written by dedicated Lego Mindstorms enthusiast David Perdue, Competitive Mindstorms introduces the reader to the sumo-bot, provides seven projects designed to give hands-on experience in three different approaches to robotic sumo (small-and-fast class, medium class, and big-sumo strategies), presents extensive discussions of Lego pieces and building methodology, programming techniques, and much more. Black-and-white photographs illustrate the building instructions in this go-to guide especially friendly to beginners but packed with valuable tips, tricks, and techniques for the seasoned Lego Mindstorms hobbyist as well.

The Lego company has a Mindstorms product line that lets you build Lego models with simple programmable electronics. Easy but fascinating robots. Perdue offers you third party support, in the form of advice that is independent of the company. This book is an amalgam of neat assembly instructions and diagrams of robots to build, with accompanying source code to control them.

The Lego library is easy enough to understand. You code in C, and link to that library. So the outlook is procedural, not object oriented. But for the code examples shown, and for any code that you are likely to write, their sizes are small enough that a procedural approach is perfectly adequate. And with less overhead than an object oriented outlook.

A nice aspect is that you can tinker with both the hardware and software, in tight feedback design loops of changing something and testing it. In other projects, often it might be purely software. Which may not appeal to you, if you're the sort who is attracted to robotics.

The is a good introductory book into the limits of computation. The book introduces the major concepts and vocabulary in a very easy to understand way. However that is the limit to this book on limits. If you are looking for non-technical information, then this may well be the book for you.

If you are looking for proofs, answers to your homwork problems, or rigor, you will be disappointed. The author states many conjectures few have proofs. From the conjectures he uses easily understood arguments to make his points. The conjectors are in fact true, but you will have to look elsewhere to find proofs.

The reasons I gave 4 stars instead of 5 are twofold. Although the book is pretty good, the writing seems a bit quirky at times. I would have liked to have seen a bit more rigor. Although I can understand wanting the book to be as simple as possible, but many of the proofs are not very difficult and could have been included (for example the halting problem).

This book is a masterpiece! It can be read on many levels and should be a must for anyone who knows how to read and think. The layman will get a gripping and very accessible account of the limits of computing in particular, and the boundaries of knowledge in general. The professional will be able to see, in a nutshell, and explicitly, what he or she or it already knew, but did not really FEEL. But note that this book does not put down computers, but shows the intrinsic limitation of all knowledge. It should have been subtitled: `What EVEN computers can't do'.

This another nice book from David Harel, the author of the delightful
'Algorithmics : the spirit of Computer Science', which introduces the
general reader to the limits of computation (and hence the limits of
what computers can do).

Harel, who's a renowned figure in the field of Theoretical Computer Science,
has the ability to write and explain in a way that makes things seem
wonderfully clear, and indeed it is only such authors who can write good
books for the general reader.

This small (240 pages) book is quite ambitious in its coverage of topics -
starting off with the notion of an algorithm, it goes on to discuss
Efficiency and correctness, Turing machines, Finite state machines,
Decidability, Computability, Complexity, NP-completeness, Recursion,
Parallel algorithms, Probabilistic algorithms, and even touches upon
Quantum Computing and Artificial Intelligence !!

All this is done with almost no mathematics, at least hardly any beyond
high-school level. The reader is gently introduced to some of the most
celebrated problems of Computer Science, and he/she can get a feel of
the nature of this exciting and interesting field.

Throughout the book, the author keeps underscoring the fact that no matter
how far technology progresses, there'll always be problems that we can't
solve cheaply, or can't solve at all, or can't ever know whether they
can be solved or not (!!), ie he stresses that there are problems that
are 'beyond computers', which cannot be tamed by more and more processing
power or any other technological advancements.

This book covers pretty much the same range of topics as Harel's earlier
book, 'Algorithmics : the spirit of Computer Science', but in only half
the number of pages, and with a heavy emphasis on the 'limitations' of
computers, which actually are limitations of our knowledge rather than
of the machines themselves.

How does it compare with the eariler book ? Well, it's more uptodate,
since it was published in 2000, whereas the other one was in 1992 -
so here you find buzzwords like 'Java', 'Dotcom', 'Quantum Computing',
etc, which you wouldn't find in the earlier book, but on the whole
i prefer the earlier one, since it had a little more detail, made you
think a little more, and even had exercises for those who were interested
in probing further.

So all in all, if you want a light, breezy introduction to the basic ideas
of Theoretical Computer Science which doesn't demand too much concentration,
this is a good choice, but if you're willing to put in some time & effort
& enjoy puzzles & logical thinking, then you'll find Harel's other book,
'Algorithmics : the spirit of Computer Science' much more rewarding.

The book serves its purpose of providing a introduction to the various technologies that make up data mining. There are three main topic sections. The first gives an overview of the technologies involved such as fuzzy logic, bayesian probability, and neural networks. The second topic area is more concentrated and focuses on how data mining works. This involves utilizing clustering, association, and classification of data. The final section covers advanced topics in web, spatial, and temporal mining. The only complaint that I would have is that most of the coverage at least in section one is cursory and one needs other reference books for serious work in the field. A very strong feature of the book is that pseudocode algorithms are offered in many sections.

Currently I am taking a Machine Learning course. This book is really helpful and intuitive. My friends who are studying Bioinformatics also found it useful.

All algorithms are presented in pseudo-code.

Dunham gives a clear explanation of the main ideas in data mining. It's a concise book, directed towards the researcher or programmer. Space considerations meant that some topics are only briefly but succinctly covered, like fuzzy logic.

More details are provided about neural networks, genetic algorithms and similarity measures. Bayesian classifications also get a good mention. Other classification measures involve distance-based methods to define clusters. For clustering, you should note that exactly what goes into a given cluster can be rather subjective. It could depend on your choice of metric.

There is a fair amount of maths. Accessible to someone with a couple of years of university level maths, especially involving linear algebra.

The section on Web mining is especially interesting. The Web is probably the largest database in the world. Certainly the most accessible. But with different characteristics from many other databases. Web data might be wrong, deliberately or otherwise. And some websites might be link farms, that try to pump up page rankings. Other databases simply don't have this concern about their contents. Dunham explains Google's PageRank and a competing idea from IBM.

The algorithms are given in pseudocode. Which should not be a problem to an experienced programmer. Translating these into your choice of language is (or at least it should be) a lesser conceptual task than understanding the methods themselves. Or devising new methods. The book also aids the latter. Dunham's descriptions of the overall logic behind each algorithm is a good lead into what is needed in construction new ones.

I used Ken Castleman's first edition in 1979 that same year to get a thorough and fast mastery of digital image processing. The book is highly recommended, because I feel that if a book is easy to read and gets the job done of educating the reader as quickly as possible, and it is actually a pleasure to go through, then it is a winner. This book is one of the rare technical books I can recommend this way. The price tag of 105 is hefty, but many books today are outlandishly priced. Even so, if the budget allows only one or two Digital Imaging texts, this would be one I would definitely pick.

This book helped me gain the basic knowledge in digital image processing. Although it doesn't have any sample programming code, the description and the math functions in the book are good enough for me to understand the different imaging processing techniques and finish my image processing programming projects at work.

This book covers most of the elements you will ever need or want to know about image processing. It presents the algorithms, mathematics, and logic behind all major techniques. It has enough math to scare off any casual reader, in enough detail to satisfy anyone seriously interested in image processing. The only bad thing about the book is the cost. If it were priced slashed to $60 or less, I'd have given it 5 stars. If you plan on doing any serious image processing in your career, it is worth the money. If you only need it for a class and not your career, then you are probably better off reading it with a formerly-wealthy classmate.

This book is a real treasure chest of ideas for an amateur robot builder. It does not show solutions in enough detail to copy them, but it points you in the right direction.

For me some of the most interesting topics covered were:
- Motorola M68332 based general purpose robot controller board,
- Introduction to different robot competitions,
- Simplified image processing solutions,
- Walking robots and evolutionary programs to control the gait,
- Examples of real life robots.

After I have read the book contents, I think it is a great book fo me. From this book, I can get many new knowledge, including many field. I want this book to guide my student to take part in
robot contest.

Hi, so far the book is a little dissapointeing, because is to much centered to a commercial hardware (EyeCon) that is somewhat hard to adquiere to a group that has a very low budget for that kind of projects... but is important to have a reference. That is why it deserve a 3. But also supply a very important source of information for sensors and actuators design. That is why i gave an average of 4 stars.

Best regards, and thanks for all the effort you made for me enjoying this book.

I have read some excellent books and survey papers describing particular domains of the evolutionary computation field, but this book try to give us a "Unified Approach" of this field.
I find very interesting the author's idea to model the evolutionary process with a dynamic (nonlinear!)system so that "...given particular initial conditions the system follows a trajectory over time through a complex evolutionary state space. One can than study various aspects of these processes such as their convergence properties, their sensitivity to initial conditions, their transient behavior and so on."[pg. 2 of the book]. And, I can add, why not "chaotically behaviors".

Prof. Alexandru Serbanescu, Ph.D.,
IEEE Senior Member of CAS and Computational Intelligence Societies

This book shows a unified view of genetic algorithms, genetic programming, and related fields. It includes some discussion of the historical development of the disciplines and presents the theory behind them.

However, while it does fit an undergrad course, it is not a book for practitioners, since most algorithms are not explained in detail.

De Jong shows how to apply the metaphor of biological evolution to many problems outside biology. He shows that in some other context, there are a few crucial issues to be determined. Firstly, what is an individual? Perhaps a particular algorithm, with certain parameter values. Then, what does it mean to have competing individuals? What is the system within which you are determining the "fitness" of an individual. Finally, how to successful individuals pass on their traits, and can these be mixed between such individuals? The latter is of course the analog of sexual reproduction.

The text describes evolutionary algorithms can be deployed as problem solvers, if you can settle the issues in the previous paragraph. These set the stage for the bulk of your computations.

The genesis of this book was a conference in May 1995 which brought together many of the main players in the CORONA project at the time it was declassified. The book contains chapters by different authors on specific aspects of the project including its historical context, technical development, impact of the resulting intellignce, etc. A short chapter on the corresponding Soviet "Zenit" project is also included. The different chapter authors bring interesting perspectives and specialist knowledge at the expense of some repetition. The photographs are also very nice. This is a more scholarly and comprehensive treatment than the Peebles book on the same subject.

Of the 3 books on this subject which I have so far read, this was the first book I obtained and read. The book is a compilation of papers/speeches of a meeting. The most accurate picture can be gained by having all three books (sorry, but more $ to Amazon, and you may tire of seeing certain images again). This book represents rare original source material from cold war history. There are some good pointers in the Appendix. Careful reading of papers talk about computing (++) and what is now regarded as "virtual reality" (++). Day's book has a chapter on the Soviet Zenit program lacking in other books (++).

Peebles' Corona book has more chapters on the human side of the recovery and process (minor Day--).

The most technical, expensive, longest-delivery time, and most professional is McDonald's ASPRS book on this same meeting. A chapter details the Corona earth model (++, math). The appendices include redacted original reports (++) and some marginally reproduced space images (++). The book also has a pointer to the breast cancer X-ray ID which the NRO/CIA claim to have released. These will not be found in Day.

>This book is less a narrative and more a series of accounts byindividuals throughout the life of the CORONA project. As such, itreads a bit differently from a single-author non-fiction book. However, this form adds detail, personal perspective, and color to the account of the project. I hope that future books can be written using this material and the newly declassified government material to write a full account of the CORONA program and its heirs and its impact to international affairs. END