Book Description
This book provides the first clear, comprehensive, and accessible account of complex adaptive social systems, by two of the field's leading authorities. Such systems--whether political parties, stock markets, or ant colonies--present some of the most intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations, Complex Adaptive Systems focuses on the key tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems. It provides a detailed introduction to concepts such as emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback. It also demonstrates how complex adaptive systems can be explored using methods ranging from mathematics to computational models of adaptive agents.
John Miller and Scott Page show how to combine ideas from economics, political science, biology, physics, and computer science to illuminate topics in organization, adaptation, decentralization, and robustness. They also demonstrate how the usual extremes used in modeling can be fruitfully transcended.
Customer Reviews:
Annie Wu -- Book #1.......2007-08-10
I am a purchasing agent who buys books for my faculty, and as far as I know, this faculty member is very impressed with this particular book.
The Emergence of Convergence .......2007-08-04
At the time of writing this review, this book isn't searchable through Amazon, that's too bad because if you're reading the reviews wondering if it's worth buying, just browsing through any page from the intro or appendix B would clearly resolve any remnant hesitation. This book is a must have for anyone even remotely interested in complex adaptive systems. Scott Page and John Miller dress the landscape and state of the art of computational social science, the issues are motivated from the ground up and the existing approaches to resolve them explicitly detailed, yet using clear and jargon free language. For example, descriptions of the many concepts repeatedly used in the scientific method (of CAS et al) such as ergodicity or optimization theory are refreshing and insightful, simply stuff you don't get from textbooks, but rather that one would learn over years of experience doing.
In summary, the authors are handing us an expert summary of literature and developments of a complex field in a concise, fun and delightful read, it would be a shame to miss it.
Book Description
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics.
Customer Reviews:
Good Theoretical GA Textbook.......2005-05-06
This book primarily deals with the theoretical side of genetic algorithms. If you are looking for practical knowledge of how to implement a GA you should look elsewhere. For all intents and purposes this is a textbook. It's heavy on theory and proofs, but doesn't always explain everything in depth (that's what class time is for). There are problems at the end of each chapter that can be assigned to students.
There are case studies of many academic projects that seem to drone on forever and aren't really that useful in helping you learn how to write your own GA. Chapter 1 gives an overview and provides all of the appropriate terminology. Chapter 5 gives an high-level overview of how to implement a GA. Those are the 2 must-read chapters, all of the others can be used as torture for CS students.
To recap, if you're teaching a class in artificial intelligence this book is good. If you're trying to figure out how to implement a GA to solve a practical problem not so good. That evens out to 3 stars for my rating. I recommend searching the web, there are a few good sites on GA programming.
Not for beginners.......2004-02-04
I have an engineering degree, and I found this to be a little tough to follow for two reasons:
1. Not enough step by step prodecure especially at the beginning. Mitchell is too quick to start with the math formulas. It turns out that Genetic Algorithms are fairly straight forward and easy to follow, but you have to read this book twice before you "get it" because Mitchell clouds the discussion with proofs and mathematical representations of systems. It is tough to follow.
2. Mitchell does a poor job of selecting meaningful examples to illustrate the points. A nice simple set of examples where the average person easily picture the system would have been delightful. Instead this author chooses to illustrate the Genetic Algorithms through uncommon neural networks amoung other exotic applications. I found myself struggling to understand both the example (I didn't know a thing about neural networks!) and the genetic algorithm.
When buying an Introduction type book, I expected it to be more 'down to earth'. this book is for advanced minds!
An introduction and much more.......2004-01-26
First it must be said that the book is not an introduction that the non-scientist will easily understand. Some knowledge of computer programming is assumed. It acknowledges this in the last paragraph of the preface. Many of the notations in the book are unfamiliar to business or financial readers. There is no mathematics beyond algebra so the aforementioned prerequisites are the main hills to climb.
Mitchell's book is an overview of genetic algorithm analysis techniques as of 1996. The author gives a history of pre-computer evolutionary strategies and a summary of John Holland's pioneering work. A description of the basic terminology is presented and examples of problems solved using a GA (such as the prisoner's dilemma). The second chapter discusses evolving programs in Lisp and cellular automata. Also included in this chapter is a discussion of predicting dynamical systems. This was the section that has the most interest for me. Also interesting was the summary in this chapter about putting GAs into a neural network so that the ANNs could evolve.
The fifth chapter discusses when to employ a GA for maximum success. I appreciate the clearly thought out discussion of when to choose a GA for a problem. Sometimes authors of these types of books mimic the man with a hammer that thinks everything looks like a nail.
A Great Introduction to Genetic Algorithms.......2002-12-07
This is a great place to start to learn about genetic algorithms. The writing is clear and not bogged down by jargon. The book is not overly technical; it is written for the layman and has a casual conversational style that is a pleasure to read.
About half of the book is devoted to presenting examples of studies that have used genetic algorithms. These examples are interesting in themselves and also serve to illustrate the variety of genetic approaches that are available. The book also presents conflicting points of view of experts about which algorithms work best and why. This is helpful in combatting the impression that a beginner sometimes gets that everything is simple and all the answers are known.
Good introduction for such a short book.......2002-04-07
Although short, this book gives a good introduction to genetic algorithms for those who are first entering the field and are looking for insight into the underlying mechanisms behind them. It was first published in 1995, and considerable work has been done in genetic algorithms since then, but it could still serve as an adequate introduction. Emphasizing the scientific and machine learning applications of genetic algorithms instead of applications to optimization and engineering, the book could serve well in an actual course on adaptive algorithms. The author includes excellent problem sets at the end of each chapter, these being divided up into "thought exercises" and "computer exercises", and in the latter she includes some challenge problems for the ambitious reader.
Chapter 1 is an overview of the main properties of genetic algorithms, along with a brief discussion of their history. The role of fitness landscapes and fitness functions is clearly outlined, and the author defines genetic algorithms as methods for searching fitness landscapes for highly fit strings. An elementary example of a genetic algorithm is given, and the author compares genetic algorithms with more traditional search methods. The author emphasizes the unique features of genetic algorithms that distinguish them from other search algorithms, namely the roles of parallel population-based search with stochastic selection of individuals, and crossover and mutation. A list of applications is given, and two explicit examples of applications are given that deal with the Prisoner's Dilemna and sorting networks. The author also gives a brief discussion as to how genetic algorithms work from a more mathematical standpoint, emphasizing the role of Holland schemas. The reader more prepared in mathematics can consult the references for more in-depth discussion.
The next chapter stresses the role of genetic algorithms in problem solving, beginning with a discussion of genetic programming. Automatic programming has long been a goal of computer scientists, and the author discusses the role of genetic programming in this area, particularly the work of John Koza on evolving LISP programs. In addition, she discusses the current work on evolving cellular automata and its role in automatic programming. The latter discussion is more detailed, this resulting from the author's personal involvement in artificial life research. Those interested in time series prediction tools will appreciate the discussion on the use of genetic algorithms to predict the behavior of dynamical systems, with an example given on predicting the behavior of the (chaotic) Mackey-Glass dynamical system. The author also gives applications of genetic algorithms in predicting protein structure, an area of application that has exploded in recent years, due to the importance of the proteome projects. The area of neural networks has also been influenced by genetic algorithms, and the author discusses how they have replaced the familiar back-propagation algorithm as a method to find the optimal weights.
Chapter 3 is more in line with what the author intended in the book, namely a discussion of the relevance of genetic algorithms to study the mechanisms behind natural selection. She discusses the "Baldwin effect", which gives a connection between what an organism has learned (a small time-scale process) to the evolutionary history of the Earth (a long time-scale process). A simple model of the Baldwin effect is given using a genetic algorithm, along with a discussion of the Ackley-Littman evolutionary reinforcement learning model, which involves the use of neural networks, and which is another computational demonstration of the Baldwin effect. In addition, the author discusses models for sexual selection and ecosystems based on genetic algorithms. These are the "artificial life" models that the author has been involved in, and she gives a very understandable overview of their properties.
Chapter 4 should suit the curiosity of the mathematician or computer scientist who wants to understand the theoretical justification behind the use of genetic algorithms. Again employing the Holland notion of schemas and adaptation as a "tension between exploration and exploitation", the author formulates a mathematical model, called the Two-Armed Bandit Problem, of how genetic algorithms are used to study the tradeoffs in this tension. The level of mathematics used here is very elementary with the emphasis placed on the intuition behind this model, with only a sketch of the model's solution given. To address the role of crossover in genetic algorithms, the author discusses in detail a class of fitness landscapes, called "Royal Road functions" that she and others have developed. The performance of the genetic algorithm employed is then compared against the three different hill-climbing methods. Formal mathematical models of genetic algorithms are also discussed, one of which involves dynamical systems, another using Markov chains, and one using the tools of statistical mechanics. The latter is very interesting from a physics standpoint but is only briefly sketched. The interested physicist reader can consult the references given by the author for further details.
Practical use of genetic algorithms demands an understanding of how to implement them, and the author does so in the last chapter of the book. She outlines some ideas on just when genetic algorithms should be used, and this is useful since a newcomer to the field may be tempted to view a genetic algorithm as merely a fancy Monte Carlo simulation. The most difficult part of using a genetic algorithm is how to encode the population, and the author discusses various ways to do this. She also details various "exotic" approaches to improving the performance of genetic algorithms, such as the "messy" genetic algorithms. One must also choose a selection method when employing genetic algorithms, and the author shows how to do this using various techniques, such as roulette wheel and stochastic universal sampling. In addition, genetic operators must also be chosen in implementing genetic algorithms, and the author emphasizes crossover and mutation for this purpose. Lastly, the values of the parameters of the genetic algorithm, such as population size, crossover rate, and mutation rate must be chosen. The author discusses various approaches to this. Although brief, she does give a large set of references for further reading.
Book Description
It is now clear that the brain is unlikely to be understood without recourse to computational theories. The theme of An Introduction to Natural Computation is that ideas from diverse areas such as neuroscience, information theory, and optimization theory have recently been extended in ways that make them useful for describing the brains programs. This book provides a comprehensive introduction to the computational material that forms the underpinnings of the currently evolving set of brain models. It stresses the broad spectrum of learning models--ranging from neural network learning through reinforcement learning to genetic learning--and situates the various models in their appropriate neural context.
To write about models of the brain before the brain is fully understood is a delicate matter. Very detailed models of the neural circuitry risk losing track of the task the brain is trying to solve. At the other extreme, models that represent cognitive constructs can be so abstract that they lose all relationship to neurobiology. An Introduction to Natural Computation takes the middle ground and stresses the computational task while staying near the neurobiology.
Customer Reviews:
Very informative - but you'll need an icepack for your head.......2002-04-16
Fifteen years ago I did research in this area and had just completed a math degree. I thought it would be interesting to get back up to speed.
I found the book fascinating, tremendous work has been done in this field and this is a good broad treatment of it. For anyone who is into computer science but has never studied the brain it will be a tremendous eye opener.
But boy, was it hard work. I found I had to read & re-read sections just to understand some of the math involved. In fairness the book does cover everything you need but if you have never done college level math, or you have forgotten most of it, then don't tackle this book when you are tired!
From Caltech.......2000-10-25
This book is one of the two main books used in Caltech's Neural Computation class (the other is Introduction to the Theory of Neural Computation by Hertz, Krogh, and Palmer). This book covers a wider spectrum of learning models than most books, including Hertz, et al. It is still fairly mathematically rigorous, although not as much as Hertz, et al. It is ideal for somebody who wants a fairly mathematically rigorous description of the subject, but also wants something more comprehensive than Herts, et al.
Book Description
Introduction to Evolvable Hardware: A Practical Guide for Designing Self-Adaptive Systems provides a fundamental introduction for engineers, designers, and managers involved in the development of adaptive, high reliability systems. It also introduces the concepts of evolvable hardware (EHW) to new researchers in a structured way. With this practical book, you’ll be able to quickly apply the techniques presented to existing design problems.
Book Description
Mobile Robotics: A Practical Introduction (2nd edition) is an excellent introduction to the foundations and methods used for designing completely autonomous mobile robots. A fascinating, cutting-edge, research topic, autonomous mobile robotics is now taught in more and more universities. In this book you are introduced to the fundamental concepts of this complex field via twelve detailed case studies that show how to build and program real working robots. Topics covered in clued learning, autonomous navigation in unmodified, noisy and unpredictable environments, and high fidelity robot simulation. This new edition has been updated to include a new chapter on novelty detection, and provides a very practical introduction to mobile robotics for a general scientific audience. It is essential reading for 2nd and 3rd year undergraduate students and postgraduate students studying robotics, artificial intelligence, cognitive science and robot engineering. The update and overview of core concepts in mobile robotics will assist and encourage practitioners of the field and set challenges to explore new avenues of research in this exiting field. The author is Senior Lecturer at the Department of Computer Science at the University of Essex. "A very fine overview over the relevant problems to be solved in the attempt to bring intelligence to a moving vehicle." Professor Dr. Ewald von Puttkamer, University of Kaiserslautern "Case studies show ways of achieving an impressive repertoire of kinds of learned behaviour, navigation and map-building. The book is an admirable introduction to this modern approach to mobile robotics and certainly gives a great deal of food for thought. This is an important and though-provoking book." Alex M. Andrew in Kybernetes Vol 29 No 4 and Robotica Vol 18
Customer Reviews:
The book provides a very good introduction to mobile robots........2000-04-30
This is a very good introductory book on mobile robots. It assumes no background of the readers in the subject. Chapter 3 provides a introduction to the hardwared aspects including sensors and actuators. Chapter 4 on machine learning by robots is also very very interesting. The case studies presented in chapter 5 and 6 will be very much useful to the new-entrants of this discipline. Chapter 7 on analysis of robot behaviour is important for the researchers working in the discipline.I like the book for its simplicity in presentation and direct reference to the points to be discussed, rather than providing a lengthy introduction to the topics. The phrase "A practcical introduction" in the title is really worthwhile for the book.
Average customer rating:
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Adaptive Systems: An Introduction (Systems & Control: Foundations & Applications)
Iven Mareels , and
Jan Willem Polderman
Manufacturer: Birkhäuser Boston
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ASIN: 0817638776 |
Customer Reviews:
A review.......2000-03-16
This book is a well written introduction into the area of adaptive systems. The text is fairly readable and the results are well presented. I would say that the book reaches its aim of providing an introduction into the area.
The best feature of the book is the chapter on Averaging Analysis for Adaptive Systems. Chapters 3-5 are quite good as well. My only gripe with the book is the small number of errors-typos and a reader would be well advised to check out the errata sheet available on one of the author's webpage (I forget with one).
I would have no problem in recommending the book to a Phd student as an introduction into adaptive systems.
Average customer rating:
- An Introduction to fuzzy control
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An Introduction to Fuzzy Control
Dimiter Driankov ,
Hans Hellendoorn , and
Michael Reinfrank
Manufacturer: Springer-Verlag
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A Course In Fuzzy Systems and Control
ASIN: 0387563628 |
Book Description
Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic to compute an appropriate control action. These fuzzy knowledge based controllers can be found either as stand-alone control elements or as integral parts of distributed control systems including conventional controllers in a wide range of industrial process control systems and consumer products. Applications of fuzzy controllers have become a well established practice for Japanese manufacturers of control equipment and systems, and are becoming more and more common for their European and American counterparts. The main aim of this book is to show that fuzzy control is not totally ad hoc, that there exist formal techniques for the analysis of a fuzzy controller, and that fuzzy control can be implemented even when no expert knowledge is available. Thus the book is mainly oriented toward control engineers and theorists rather than fuzzy and non-fuzzy AI people. However, parts can be read without any knowledge of control theory and may be of interest to AI people. The book has six chapters. Chapter 1 introduces two major classes of knowledge based systems for closedloop control. Chapter 2 introduces relevant parts of fuzzy set theory and fuzzy logic. Chapter 3 introduces the principal design parameters of a fuzzy knowledge based controller (FKBC) and discusses their relevance with respect to its performance. Chapter 4 considers an FKBC as a particular type of nonlinear controller. Chapter 5 considers tuning and adaptation of FKBCs, which are nonlinear and so can be designed to cope with a certain amount of nonlinearity. Chapter 6 considers several approaches for stability analysis of FKBCs in the context of classical nonlinear dynamic systems theory.
Customer Reviews:
An Introduction to fuzzy control.......2000-01-10
I'm some interesting in fuzzy contro
Average customer rating:
- Not worth buying
- The worst book in my graduate course
- The book is useful to a wide audience.
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An Introduction to Fuzzy Sets: Analysis and Design (Complex Adaptive Systems)
Witold Pedrycz , and
Fernando Gomide
Manufacturer: The MIT Press
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Fuzzy Sets and Fuzzy Logic: Theory and Applications
ASIN: 0262161710 |
Book Description
The concept of fuzzy sets is one of the most fundamental and influential tools in computational intelligence. Fuzzy sets can provide solutions to a broad range of problems of control, pattern classification, reasoning, planning, and computer vision. This book bridges the gap that has developed between theory and practice. The authors explain what fuzzy sets are, why they work, when they should be used (and when they shouldn't), and how to design systems using them.
The authors take an unusual top-down approach to the design of detailed algorithms. They begin with illustrative examples, explain the fundamental theory and design methodologies, and then present more advanced case studies dealing with practical tasks. While they use mathematics to introduce concepts, they ground them in examples of real-world problems that can be solved through fuzzy set technology. The only mathematics prerequisites are a basic knowledge of introductory calculus and linear algebra.
Customer Reviews:
Not worth buying.......2004-11-18
As a student of fuzzy systems, with MS thesis in this area, I don't recommend this book. Unfortunately I used this book in my first contact with fuzzy systems, I was very frustrated.
Then I found other books and tutorials that helped me a lot to understand the subject.
The worst book in my graduate course.......2004-11-18
Don't buy this book.
As a student with one of the authors and reader of the book from cover to cover, I feel confident to make my recomendation: Don't waste your money with this book. Try IEEE Proceedings special on Fuzzy Systems, the book from Zimmerman or go directly to the articles.
The book is useful to a wide audience........2000-03-26
The book covers all aspects of fuzzy sets in a clear and highly comprehensive manner. I liked the book very much and adopted the text for our graduate students at ETCE Department, Jadavpur University.I liked all chapters of the book. I however have a special interest for the chapters 11 and 12 that cover fuzzy nurocomputing and fuzzy evolutionary computing respectively.
I believe all of you will enjoy reading this beautiful book.
Book Description
This digital document is an article from Tooling & Production, published by Nelson Publishing on September 1, 1997. The length of the article is 721 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.
From the supplier: OMRAT Control Technologies introduces Optimil, an adaptive control system which serves to optimize the productivity of CNC milling/drilling machines and machining centers, enabling machines to automatically respond to varying work conditions. Optimil further allows optimum use of operating time by freeing the operator to focus on the other aspects of the job while limiting machine wear.
Citation Details
Title: Adaptive control optimizes machining: technology realizes early promise in Optimil.(Product Announcement)
Publication:
Tooling & Production (Magazine/Journal)
Date: September 1, 1997
Publisher: Nelson Publishing
Volume: v63
Issue: n6
Page: p29(2)
Article Type: Product Announcement
Distributed by Thomson Gale
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ADC LAUNCHES UNIFIED MESSAGE PORTAL, EXTENDING BROADBAND SERVICES PLATFORM AND ENHANCING ADAPTIVE COMMUNICATION MARKETPLACE.(Company Business and Marketing): An article from: EDP Weekly's IT Monitor
Manufacturer: Millin Publishing, Inc.
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Release Date: 2005-07-28 |
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This digital document is an article from EDP Weekly's IT Monitor, published by Millin Publishing, Inc. on May 7, 2001. The length of the article is 757 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.
Citation Details
Title: ADC LAUNCHES UNIFIED MESSAGE PORTAL, EXTENDING BROADBAND SERVICES PLATFORM AND ENHANCING ADAPTIVE COMMUNICATION MARKETPLACE.(Company Business and Marketing)
Publication:
EDP Weekly's IT Monitor (Magazine/Journal)
Date: May 7, 2001
Publisher: Millin Publishing, Inc.
Volume: 42
Issue: 17
Page: 8
Distributed by Thomson Gale
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- Differential Equations, Dynamical Systems, and an Introduction to Chaos (Pure and Applied Mathematics (Academic Press), 60.)
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