Artificial Intelligence Books

provide a good and easy to understand introduction to the subject

This book gives a good introduction to the mathematical modeling of cognition and does so with a level of mathematics that is very accessible to a typical graduate student in computer science or psychology. The book could have been written using tools from measure theory but luckily it was not for a book at an introductory level. The concept of probably approximately correct is introduced early on in the third chapter of the book with efficient learning given later on in Chapter 5. Chapter 7, the best chapter of the book, discusses the idea of VC dimension, which has had many applications, such as network stability and optimization. VC dimension plays the pre-dominant theme in the rest of the book, with the book ending with an application to neural networks. There are short problem sets at the end of the chapters, and these are useful for more understanding of the concepts in the book. A very interesting book and worth the price.

I found the projects interesting and the introduction to Not Quite C NQC excellent. The author demonstrates how to connect the touch sensors and light sensors physically and then add programming code to control the logic that produces power from the central processor to a motor that powers a gear. The basics are simple to understand and the project increase in sophistication and complexity.

The book explains explains different types of gear ratios, differential gear combinations, power and speed gear ratios, pulleys, power connections, stop latch, levels, frames, sound, and how to simulate an actuator like a grabber.

The robotic behavior can be either conditional or remote controlled. I'm a programmer and this book helped me break into the world of robot programming, signal programming, multitask abstraction, and signal processing without having construct the hardware.

Lego Mindstorm is a much easier and faster way to build simple robots verses trying to construct all the hardware on your own. Each chapter has a flow chart of tasks and functionality that help explain the logic controlling the robot. I found this book a delight to read and understand.

This is a first impression, and may be upgraded later. The textual content, including appendices, is good. Unfortunately, the illustrations have inadequate contrast, and are difficult to interpret even under very bright light. This is due to extensive and unnecessary use of gray scale, both in assembly drawings and flow charts(!). While it is quite possible that Apress printed the grayscale much darker than Mr. Baum intended, I believe the decision to use gray scale at all was flawed. On the other hand, the use of isometric ("3D") projections is entirely appropriate. If reprinted with isometric line drawings (no gray scale) I'd probably rate it 5 stars (especially if I could trade in my gray scale version).

What do musicians, artists, linguists, Buddhist monks, and computers have in common? This book takles this broad scope with some very interesting revelations. The author has a Ph.D. in computer science and an undergraduate degree in eastern & western philosophy. Thus, the title and the Buddhist angle.

This books reads easily and is very entertaining. Coming from an engineering background, I appreciated the author's structured writing style. That is, he does not meander or get flowery with his words. He states his facts, makes his points, and moves on. The reader does not get overwelmed with too much detail or historical data, but an extensive bibliography is available for the curious. The plenitude of charts and illustrations is helpful and at times a necessity.

In the beginning of the book the author keeps each subject separate: one chapter dedicated to linguistics, another chapter to abstract art, etc. Slowly he begins to reveal how all these areas mesh, which left me anticipating a climatic revelation that would tie it all together. However, I found the conclusion to be somewhat anti-climatic involving the future of virtual reality and the author's own eastern religious beliefs.

If you are a musician, artist, linquists, or work with computers and you have ever wondered why you think the way you think, definitely read this book. It will open your eyes and broaden your horizons immensly. Caution: if you are looking for "how to" information, technical details, or references to the latest/greatest equipment, it's not what this book is about.

This beautifully written book offers interesting sections into the history of algorithmic thinking in the arts, and builds a concept of the computer use in art thereon, embedded in a context of indian mythology. It is the best book combining music and visual arts in this respect that I have read so far and its concepts will certainly and hopefully be quite influential on the producers of new media work. Musicians might find the chapter on serial music a little superficial, as visual artists might perceive the one on Kandinsky's work, and I am not so sure whether I agree with the author's personal "unified theory" presented in the last chapters of the book, but the strength lies in the combination, and if you are looking for a general introduction, it is a stimulating overview that serves as a great starting point for further studies. In comparison to Hofstaedters "Goedel, Escher, Bach" this one feels more relaxed and undogmatic and stays with topics of abstract language and generative grammars, instead of trying to hammer a cynical anti-spiritual pseudo-religion into your brain.

the book are many free papers on internet.

There are very theory technologies but no new contents about DEVS.

These articles originated as papers from a recent meeting so that some may be found on the web. However,the articles are extended and stringently reviewed revisions of the original papers the collection itself is of great value since it amounts to a whole more than the sum. The introductory article is not just a precis of the contents, it does an excellent job of placing discrete event modeling and simulation technology within the context of AI, software engineering, and systems engineering.

Contrary to one reviewer, the book cannot be derived in any way from what's available on the web. If you are interested in new paradigms for technology, it belongs on your bookshelf.

This book is based on the author's PhD dissertation and it shows (you can download the dissertation from the web). There is page after page of mind numbing step-by-step derivations that do not add too much to the discussion. I would have enjoyed the book more if Spears had shortened some of his derivations. I found the results interesting. Although some of the conclusions seem fairly obvious after reading the book, I think it is important that someone took the time to come up with the mathematical models to formalize things.

The empirical approach is very interesting, and I wish more people would follow and improve on Spears' ideas. Empirical studies of evolutionary algorithms are justly critized for being too limited to a few "standard" functions that do not show much about the capabilities and limitations of the algorithms. Spears took a good step in emulating the machine learning comunity and using test problem generators. With these generators, the experimenters can play around with parameters such as the multimodality or noise in a problem and make systematic empirical studies of the algorithms. Unfortunately, it is difficult to translate from those systematic studies to real life. For example, how much noise or how many peaks are in real-life problems?

Still, I would recommend to go and read this book (or the free dissertation). Skip the equations, though, and get to the point.

BTW, Dr Gordon (the first reviewer) is married to Spears, which may explain some of the excitement in her review...

This book is an essential resource for anyone studying the theoretical underpinnings of evolutionary algorithms (EAs). The book very carefully analyzes the effects of two fundamental evolutionary operators, recombination and mutation, and their interaction with evolutionary selection. This analysis significantly enhanced my understanding of EAs because of the fundamental role that these operators play. The book begins with the more traditional static analysis approach, but soon it transitions to a very exciting dynamic analysis. Just as neurophysiologists have discovered that when studying the brain it helps to view it as a dynamic process, Spears illustrates how much better we can understand EAs when using dynamic models, such as the popular Markov chain model approach. One of the best parts of the book was the creative use of problem generators for empirically testing the theory and for characterizing the classes of problems for which each EA operator is more effective. This was exciting for two reasons. For one, it encourages EA researchers to break away from myopic use of the same old test suites. Secondly, the problem characterization has tremendous potential value for practical applications of EAs.

Another of my favorite parts of the book was Spears' novel algorithm for compressing Markov chains. I particularly liked the mathematical analysis, which was both elegant and clear. Because Markov chains are widely used, e.g., in operations research, control theory, and artificial intelligence, this compression algorithm has wide-reaching implications for reducing the complexity of modeling a variety of systems.

The intended audience for Spears' book is computer scientists, mathematicians, and biologists, as well as students of evolutionary processes. To make the book accessible to such a diverse audience, the presentation is exceptionally clear and devoid of excessive jargon and obscure mathematics. Only an undergraduate level math background is required. One thing that I found mildly distracting was the repetition between chapters. The reason for the repetition was to make the chapters as self-sufficient as possible. Nevertheless, I read the book as a continuous whole and for anyone who does this I recommend skimming or skipping over the redunant portions. If this is done, the reader can maintain a high level of interest.

In conclusion, because of the valuable insights I gleaned from this book I believe it should be required reading for anyone who wishes to gain a better understanding of evolution as simulated by EAs. Spears' rigorous analyses and lucid explanations make this a delightful book to read.

The first volume provides a very broad coverage of the "evolutionary" literature. Reading this first volume will probably save you a lot of time. The evolutionary literature actually becomes quite large these days. The focus of this first volume is on broad coverage, not details although some chapters are already quite advanced.

If you need a fast coverage of the literature in evolutionary computation, this is the book. Pointers to all decisive contributions to the field are there. Reading from cover to cover might be difficult if the purpose is to introduce one to the field, but this is certainly the reference i would suggest to students and researchers new in this field. Each chapter is self-contained and references to the most important works for each chapter is provided at the end of the chapter.

Overall, this and the second volume combined do well to cover the major topics of evolutionary computation. Unfortunately, the IOP (the publisher) is not very good making the books (especially the first volume) available. I used both volumes for a course I teach in evolutionary computation. I completed the course, and most of my students received volume 2, but did not get volume 1 until well after the semester was over.

In fairness, things may have changed since this class was taught. I would STRONGLY suggest that anyone interested in the books contact the publisher prior to order to make sure they will be received in a timely manner.

The contents of these volumes used to be available free online from the IOP site. They are still on the IOP site, but you now have to pay. Pity.

The subject of this book would seem a natural one, given the evolutionary paradigm in biology. Genetic algorithms and evolutionary programming have now found use in many different fields such as physics, financial engineering, network modeling, and computational radiology, to name a few. This use will no doubt continue as computer processing power increases in the future. Although genetic/evolutionary approaches are still much more effective from a computational point of view than strict combinatorial ones, they are still very time intensive, and for many problems have yet to compete with ordinary Monte Carlo techniques. This book gives a brief overview of how evolutionary algorithms are used in bioinformatics, with emphasis on genetic sequence alignment and protein folding. The book does not offer in-depth discussion on these algorithms, but does give references where more information can be obtained. Therefore the book could be described as a literature survey, at least for the chapters that I read, which did not include those on protein folding.

The book is written for the computer scientist who wants to move into bioinformatics, and the biologist, who needs more background in these types of algorithms. Therefore, the editors of the book include two introductory chapters, one introducing bioinformatics for computer scientists, the other an introduction to evolutionary computation for biologists. The latter is more detailed, and the authors introduce the biologist to some of the elementary aspects of evolutionary computation. One interesting, but too short discussion is on the "No Free Lunch Theorem", which implies that evolutionary programs are not in any sense "universal", in that the choice of such a program will depend on the problem at hand, and in fact there may be many such programs for the problem, each with their own performance properties. The theorem is not proved in this book, but references to the proof are given. However, the proof involves a level of mathematics that a biologist would probably not have knowledge of, and so this reference would not be accessible to such a reader. In addition, the theorem has generated a lot of controversy, but the authors do point this out. The authors also discuss effectively the difference between the analytical and heuristic approaches to sequence alignment, setting the stage for later chapters in the book. The problem of local search algorithms getting "trapped" in local minima is also given a very intuitive and understandable treatment by the authors.

The book also includes a discussion on the "DNA sequence reconstruction problem". Algorithms for dealing with this problem are recommended and the the problem is presented as one in integer programming. The authors present a hybrid evolutionary algorithm for dealing with this problem. They characterize this algorithm as being hybrid since it does make use of "crossover" operators and a heuristic "greedy-improvement" method. The discussion of this algorithm is only brief, but references are given. However the main reference is not yet available as it is very recent and in press, and, although the authors do include a fairly lengthy discussion of computational experiments, without a detailed description of the algorithm or source code, their results cannot be checked or validated.

The contrast between optimization theory and evolutionary algorithms is a common theme in the book, with emphasis on the use of evolutionary algorithms to design scoring schemes for sequence alignment where optimization issues can be ignored. The difference between the optimal alignment obtained by various mathematical techniques and the correct (biological) alignment is carefully pointed out. Thus one must be able to tell whether an objective function is relevant from a biological standpoint. In chapter 5 of the book for example, the author introduces an alignment algorithm based on a combination of simulated annealing (SA), and genetic algorithms (GA), called appropriately SAGA. This chapter is the most helpful one in the book, for the author gives pseudocode for this algorithm, with Web links given for obtaining the source code. This allows the interested reader to study the efficacy of the SAGA algorithm in doing muliple sequence alignment.

The use of simulated evolution to find optimal neural networks for identifying coding regions is discussed in chapter 9 of the book. The use of genetic algorithms to assign the weights in a neural network is well-known. The authors point out a further advantage in their use, namely that evolutionary neural networks can adapt to unexpected inputs on their own, and thus do not require any intervention on the part of the user. References are given that elaborate on the power of this approach. Readers who have worked with neural networks will understand fully the need for improvements over back-propagation and the need for automatic topology selection. The authors do not show however that the function-approximation ability of neural networks, so important from both a mathematical and applications standpoint, is improved by their approach.

I like this book. Bioinformatics is a ripe area for applying evolutionary algorithms and the book provides a good overview of many different applications. Some chapters are more polished than others, but that's to be expected. The editors do an excellent job of introducing both bioinformatics and evolutionary computation to their respective audiences. I can't think of another book that makes such an effort to integrate the two communities.

I see another reviewer gave the book 3 stars. I've no idea why. The book is excellent, and has encouraged me to take a look at other papers in this area.

This is a grat book because it is multidiciplinary, but maybe content wise, it is not very original-nor focused in consciousness. There are papers dealing with squitzofrenia and ageing. Greenfield contribution presents her neuronal assemblie theory. Rose writes a great introduction to consciousness studies, and it alone pays for the entrance ticket. It is always interesting to read about Aleksanders work on artificial consciousness, and Penrose on Quantum Consciousness. The collection as a whole covers many topics, and it is a valuable contribution to consciousness studies. It is also not at all technical, so it can serve as an introductory work of the field. Again, originality and content do will not live to many expectations, but I certialy recomend the book.

While every section of science studies brain, mind, culture and psychopathology on its own grounds, this collection of essays shows how all disciplines together can shed light on each other's field of interest and solve some tough question. When I purchased this book I was looking for a reflection of mr. Rose's ideology of science, which it turned out not to be. Nonetheless, it is very relevant and quite interesting!
(I later found more of mr. Rose's thoughts in a book he edited with a Hillary Rose - his wife? - called "Alas Poor Darwin". It shows the untenability of Evolutionary Psychology. His own article in that collection is by far the best of all. Also, his "Not in Our Genes" with Richard Lewontin is supposed to be a reflection of his philosophy of science.)

This is a great book for a control engineer, not someone who wants to learn about fuzzy logic (and the author makes that clear). It has real engineering examples, and a very clear introduction to the area. Also, it covers all the major topics, has lots of examples and code that are very helpful (in matlab). I highly recommend it.... for the engineer who wants to really get a fuzzy controller working - or for someone new to the area (e.g. students who have already had a course on control systems. Chapter 2 is an especially helpful and clear tutorial... You must, however, understand signals and systems, and differential equations...

The only advantage of the book is the software examples available on author's homepage. It say little on the reasoning of fuzzy inference and the basic operations of fuzzy set. If you want to have a complete know-how guide for fuzzy control, you probably be disappointed. You would gain the maximum if your have learnt the basics from other books before dipping into its software examples. In summary, the book is not the functional part for learning fuzzy control.

Interest in inductive logic programming has waxed and waned over the last decade, but never fallen to zero. This book is a summary of what was known in the field in 1994, and much has changed since then. It can however still serve as an introduction to the field of inductive logic programming, in spite of its publication date. Most of the current research and applications of inductive logic programming has concentrated on introducing stochasticity into logic programming and on how to incorporate reasoning with numerics into the framework.

The authors emphasize the empirical aspects of inductive logic programming and its applications, but do spend the first few chapters detailing the theoretical foundations of the subject. The characterize machine learning paradigms as inductive, deductive, learning with genetic algorithms, and learning with neural nets. They rule out neural net learning as being a true learning system since it does not pass the "Michie strong criterion", i.e. learning must acquire new knowledge which must be understandable by humans. They do not elaborate on why neural nets fail to meet this criterion. Inductive learning of course is what they consider exclusively in the book, with inductive concept learning essentially consisting of the learning of how to recognize objects in the concept, the concept being a subset of objects in universal set of objects or observations. To define inductive concept learning more rigorously the authors employ the concept of a covering of an object, which means essentially that the description of the object satisfies the description of the concept. The object description is thus "covered" by the concept description. Notions of completeness and consistency of hypotheses are then introduced, with completeness being the requirement that the hypothesis cover all positive examples and no negative ones, while consistency meaning that it does not cover any of the negative examples. These ideas are then generalized to the case where background knowledge is present. Inductive logic programming systems are then defined as those that induce hypotheses in the form of logic programs. These systems are partitioned into those that learn predicates from scratch, called empirical ILP systems, and those that learn multiple predicates, called interactive ILP systems. The authors then discuss briefly the systems that were available at the time of writing. Only empirical ILP systems are considered by the authors in the book, with emphasis on the systems LINUS and FOIL, which were the dominant ones at the time of writing.

Because of its popularity and effectiveness in logic programming, the authors employ Prolog to introduce the basic theory of logic programming. Other languages have been developed since then with ILP applications in mind, one of these being Progol. Symbolic programming languages, such as Mathematica and Maple, can also be used, and very effectively. The essentials of logic programming discussed in the book have no doubt been seen by the reader, and some familiar concepts such as Horn clauses and resolution are discussed by the authors. The goal of empirical ILP then is to find a complete and consistent definition for an unknown predicate given a set of examples and background knowledge. Concept learning is viewed as a search problem, with states in the search space being concept descriptions. The goal is to find states that satisfy a quality criterion, and a learning algorithm is characterized in terms of the structure of its search space, its search strategy, and the search heuristics. The structure of the search space is characterized by a "theta-subsumption lattice", which gives the structure of the search space of program clauses, and which can be searched blindly or heuristically. Theta-subsumption provides the basis for a "bottom-up" ILP technique, namely that of the building of least general generalizations from training examples relative to background knowledge, and a "top-down" technique of the searching of refinement graphs. These techniques and the technique of inverse resolution are discussed in detail by the authors. The idea of inverse resolution will seem natural to the reader familiar with the related (but inverted) procedure in deductive (propositional) logic. Inverse resolution inverts the SLD-resolution proof procedure for definite programs.

Most of the book is devoted to an overview of the FOIL system and how it can be implemented to do practical inductive logic programming. The search routines used by FOIL are hill-climbing strategies, and the authors discuss ways that have been used to improve on these. Since this book was written, an ILP system called SFOIL has appeared that takes advantage of the view of induction of hypotheses as an optimization problem. Interestingly, SFOIL uses a generalization of simulated annealing to do this, based on Markovian neural networks. The authors also review the GOLEM ILP programming language, which is based on the notion of relative least general generalization, again a bottom-up search of the theta-subsumption lattice. Other ILP languages, such as MOBAL and MPL are also reviewed. In addition, the LINUS IPL system is reviewed, which exploits background knowledge in learning both propositional and relational descriptions. Deduction plays a major role in the LINUS system, as well as the transformation of relational descriptions to a propositional learning task. Both the FOIL and the LINUS systems are characterized with respect to refinement operators and refinement graphs, which allows a comparison of the expressiveness of their hypothesis languages and the search costs associated with these systems.

The authors also discuss how to handle imperfect data in ILP, and show the role of heuristics in doing this. Random errors in training examples and background knowledge, sparse training examples, inexact description of target concepts, and missing values in training examples all need to be dealt with when using ILP, and various techniques are oultined by the authors to do this. Several interesting applications of ILP are given in the book, including medical diagnostics, finite element methods, qualitative modeling of dynamical systems, and predicting protein secondary structure. The role of ILP in bioinformatics has taken on more importance in recent years, and this trend will no doubt continue.

Interest in inductive logic programming has waxed and waned over the last decade, but never fallen to zero. This book is a summary of what was known in the field in 1994, and much has changed since then. It can however still serve as an introduction to the field of inductive logic programming, in spite of its publication date. Most of the current research and applications of inductive logic programming has concentrated on introducing stochasticity into logic programming and on how to incorporate reasoning with numerics into the framework.

The authors emphasize the empirical aspects of inductive logic programming and its applications, but do spend the first few chapters detailing the theoretical foundations of the subject. The characterize machine learning paradigms as inductive, deductive, learning with genetic algorithms, and learning with neural nets. They rule out neural net learning as being a true learning system since it does not pass the "Michie strong criterion", i.e. learning must acquire new knowledge which must be understandable by humans. They do not elaborate on why neural nets fail to meet this criterion. Inductive learning of course is what they consider exclusively in the book, with inductive concept learning essentially consisting of the learning of how to recognize objects in the concept, the concept being a subset of objects in universal set of objects or observations. To define inductive concept learning more rigorously the authors employ the concept of a covering of an object, which means essentially that the description of the object satisfies the description of the concept. The object description is thus "covered" by the concept description. Notions of completeness and consistency of hypotheses are then introduced, with completeness being the requirement that the hypothesis cover all positive examples and no negative ones, while consistency meaning that it does not cover any of the negative examples. These ideas are then generalized to the case where background knowledge is present. Inductive logic programming systems are then defined as those that induce hypotheses in the form of logic programs. These systems are partitioned into those that learn predicates from scratch, called empirical ILP systems, and those that learn multiple predicates, called interactive ILP systems. The authors then discuss briefly the systems that were available at the time of writing. Only empirical ILP systems are considered by the authors in the book, with emphasis on the systems LINUS and FOIL, which were the dominant ones at the time of writing.

Because of its popularity and effectiveness in logic programming, the authors employ Prolog to introduce the basic theory of logic programming. Other languages have been developed since then with ILP applications in mind, one of these being Progol. Symbolic programming languages, such as Mathematica and Maple, can also be used, and very effectively. The essentials of logic programming discussed in the book have no doubt been seen by the reader, and some familiar concepts such as Horn clauses and resolution are discussed by the authors. The goal of empirical ILP then is to find a complete and consistent definition for an unknown predicate given a set of examples and background knowledge. Concept learning is viewed as a search problem, with states in the search space being concept descriptions. The goal is to find states that satisfy a quality criterion, and a learning algorithm is characterized in terms of the structure of its search space, its search strategy, and the search heuristics. The structure of the search space is characterized by a "theta-subsumption lattice", which gives the structure of the search space of program clauses, and which can be searched blindly or heuristically. Theta-subsumption provides the basis for a "bottom-up" ILP technique, namely that of the building of least general generalizations from training examples relative to background knowledge, and a "top-down" technique of the searching of refinement graphs. These techniques and the technique of inverse resolution are discussed in detail by the authors. The idea of inverse resolution will seem natural to the reader familiar with the related (but inverted) procedure in deductive (propositional) logic. Inverse resolution inverts the SLD-resolution proof procedure for definite programs.

Most of the book is devoted to an overview of the FOIL system and how it can be implemented to do practical inductive logic programming. The search routines used by FOIL are hill-climbing strategies, and the authors discuss ways that have been used to improve on these. Since this book was written, an ILP system called SFOIL has appeared that takes advantage of the view of induction of hypotheses as an optimization problem. Interestingly, SFOIL uses a generalization of simulated annealing to do this, based on Markovian neural networks. The authors also review the GOLEM ILP programming language, which is based on the notion of relative least general generalization, again a bottom-up search of the theta-subsumption lattice. Other ILP languages, such as MOBAL and MPL are also reviewed. In addition, the LINUS IPL system is reviewed, which exploits background knowledge in learning both propositional and relational descriptions. Deduction plays a major role in the LINUS system, as well as the transformation of relational descriptions to a propositional learning task. Both the FOIL and the LINUS systems are characterized with respect to refinement operators and refinement graphs, which allows a comparison of the expressiveness of their hypothesis languages and the search costs associated with these systems.

The authors also discuss how to handle imperfect data in ILP, and show the role of heuristics in doing this. Random errors in training examples and background knowledge, sparse training examples, inexact description of target concepts, and missing values in training examples all need to be dealt with when using ILP, and various techniques are oultined by the authors to do this. Several interesting applications of ILP are given in the book, including medical diagnostics, finite element methods, qualitative modeling of dynamical systems, and predicting protein secondary structure. The role of ILP in bioinformatics has taken on more importance in recent years, and this trend will no doubt continue.