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

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

Product Description

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

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

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

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

Some of the areas we publish in:

-Biomedicine
-Oncology
-Artificial intelligence
-Databases and information systems
-Maritime engineering
-Nanotechnology
-Geoengineering
-All aspects of physics
-E-governance
-E-commerce
-The knowledge economy
-Urban studies
-Arms control
-Understanding and responding to terrorism
-Medical informatics
-Computer Sciences ... Read more

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

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

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

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

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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

Customer Reviews (3)

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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ARTIFICIAL INTELLIGENCE AND SOFTWARE ENGINEERING Understanding the Promise of the Future

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

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

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

Customer Reviews (1)

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

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

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

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

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

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

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

Product Description
Nearly all AI books are scholarly and theoretical. Most concentrate on basic research questions and issues. This book avoids those issues and controversies and instead deals with where we are now in terms of practical applications and real applied potential. The book represents the first collection of applications selected on the basis of how successful they have been. Areas include, manufacturing and design, computer assisted instruction, national defense, robotics, airline industry, software engineers, etc. ... Read more

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This text provides an introductory-level overview of artificial intelligence (AI). It features clear presentation of principles integrated with short, workable programs which are designed to help students to learn by experimentation and to develop an intuitive understanding of the subject. The book features: expression of AI theory in programs written with common LISP, the established standard for the field; new chapters on common sense reasoning and neural networks; new sections on global variables, LISP structures, LISP association lists, iterative deepening, constructing decision trees, genetic algorithms and embedded AI; expanded coverage of expert systems; updated programming style in example programs, plus many new example programs; and coverage of additional Common LISP features. ... Read more

Customer Reviews (3)

A must
The 2nd edition is a real complete book on AI elements.
The book is for undergraduate or first year graduate, and
it is not required a full background in calculs or algebra.
All chapters require a pratice works on lisp example, in order
to be most effective.
Tanimoto written lisp examples prior to the language standardization,
so source codes could be a little re-worked.
Although all text example are based on lisp, it would be easy
to applay theory to other programming language as C/C++, tcl/tk, etc.
Finnaly, the book is "a must" for people real interested on AI.

VERY GOOD BOOK, except...
I learned to programming Common Lisp using Tanimoto's book (in conjunction with Winston Horn's "Lisp"). The book's writing is superb, and the examples are very well thought out and implemented.

One word of caution: the book was written before the complete standardization of Common Lisp. So some of the functions, such as those specific to I/O and FEXPR will not work on current Common Lisp implementations (such as GCL). But all of these functions can be worked around easily.

I'll still give this book a five star. The book is particularly good for self-study. So I recommend it to any AI enthusiast.

A very broad treatment of AI Fundamentals
Tanimoto provides a very broad treatment of AI techniques.As such dicussions are often brief.There is an outstanding section on Computer vision.Knowledge representation is also well covered.The author presents highly idiomatic examples of Lisp code.This book would be ideal for anyone not familiar with AI techniques who wants to do AI research and/or development. ... Read more

Product Description

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

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

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

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When we play the ancient and noble game of chess, we grapple with ideas about honesty, deceitfulness, bravery, fear, aggression, beauty, and creativity, which echo (or allow us to depart from) the attitudes we take in our daily lives. Chess is an activity in which we deploy almost all our available cognitive resources; therefore, it makes an ideal laboratory for investigation into the workings of the mind. Indeed, research into artificial intelligence (AI) has used chess as a model for intelligent behavior since the 1950s. In Chess Metaphors, Diego Rasskin-Gutman explores fundamental questions about memory, thought, emotion, consciousness, and other cognitive processes through the game of chess, using the moves of thirty-two pieces over sixty-four squares to map the structural and functional organization of the brain.

Rasskin-Gutman focuses on the cognitive task of problem solving, exploring it from the perspectives of both biology and AI. He examines concept after concept, move after move, delving into the varied mental mechanisms and the cognitive processes underlying the actions of playing chess. Bringing the game of chess into a larger framework, he analyzes its collateral influences that spread along the frontiers of games, art, and science. Finally, he investigates AI's effort to program a computer that could beat a flesh-and-blood grandmaster (and win a world chess championship) and how the results fall short when compared to the truly creative nature of the human mind. ... Read more

Customer Reviews (3)

The State of Chess Since Deep Blue's Victory over Kasparov
The author covers the waterfront since "Deep Blue" soundly beat Garry Kasparov but in a very much hit or miss fashion. Even with the announcement in the title that the book is about "chess metaphors," the organization of the book suffers from too much jumping about and too little apparent actual knowledge about chess thought per se. It is not entirely clear what he expected to accomplish in the book as a whole? The results are a kind of parallel analysis of several loosely connected topics that should they have been better integrated would have made for a much more interesting read: The key topics that were introduced and in some cases glossed over, were: how the brain works, the psychology of competitive board games, and a brief history of how computer chess has evolved, with a little AI thrown in at the end just for good measure.

Having studied, used and taught applications to neural networks, I was particularly interested in how chess-playing algorithms have evolved in this direction since my teaching days, but was not entirely enlightened by this rather uneven treatment of the subject. One thing that has become clear from the author's analysis is that chess-playing computers are still only as good as the humans writing the programs. We are indeed a long way from the era when a computer can simulate human thought processes, and then independently act on them -- which is the most often scenario called up in the imagination when one thinks of a computer playing chess machine.

Instead of a computer that thinks like a top echelon creative chess master, we are told by this author that what we get instead is one that substitutes inelegant power and brute electronic force (its multiple-ply search algorithms can evaluate up to as many as 200 billion moves per second!) for human creativity and intuition. This number-crunching behemoth that (again) we can imagine crashing along the chessboard like a medieval armored Knight, with over-sized clanging metal feet is not exactly what we had in mind as the replacement for the world's greatest chess masters.

As one who not only has watched this spectacle unfold over the last forty years (I still have an operational version of one of the earliest computer chess machine called "Boris," and all of the generations up through Sargon I-IV and had my class program a prototype chess machine using the BASIC computer language), I cannot say that I am pleased with the direction that computer chess has taken, or with the way this book has characterized that progress. Sadly, "brute computer force" seems to be the name, the beginning, and the end of the game.

Nowadays, unless you have memorized the first forty "book moves" for at least several dozen chess openings, you can expect to get soundly trounced every time (and very early out of the starting gate if you are the least bit careless).What I have had to resort to just to keep the games interesting, is pick one or two novel openings and use the machine as a heuristic tutor to deepen my knowledge about the variations in those particular openings. But even when you discover a hole in its logic, the computer still has a tremendous advantage. It can sense your level of play, and then proceed to set "book traps" for you. And then (if it should have erroneously underestimated your strength, which it rarely does), it still has time to recover and angle for a draw, which it can ensure itself 99.9% of the time even in the rare instances when it gets outplayed in the middle game.Risky moves and sacrifices, the very heart and soul of chess, are a "no-no" with chess computers of even the least sophistication. They are so accurate and so brutal and unforgiving that on balance it, and the tendency to seek out "drawish" endings, discourages the desire to continue playing them.

Unfortunately, the computer could care less about style or book theory, except in the openings (and there it plays with such mathematical accuracy that it can be disheartening). According to this author, it accomplishes its superhuman feats by simply using a very tight algorithm to count up the values of the chess pieces at any given position, analyzes a few million moves, and then re-computes them over and over again after each move.

I wish I had that algorithm, especially the positional component of the one most computer programs use. The fact that the computer is entirely free of prejudice and doctrine seems to give new meaning to a famous quip by the great Cuban Grandmaster Raul Jose Capablanca. When asked how many moves ahead he looks, Capablanca answered "only one, the best one."

Four Stars

"An overview of overviews, if little else"
Rasskin-Gutman hoped to write a book not about how to apply AI to chess, but create a sort of workbook of ideas and suggestions on what might be posssible to do so. "Basically, I have put together in a single volume those ideas, elements, facts, and surprises I would have loved to find in a bookstore 20 years ago.... If I have reshaped chess in new dimensions within the natural world and within the promising scenarios of AI, I have fulfilled my objective."

As a result, it is difficult to disagree with anything in A. Menon's excellent review of the book here on Amazon, and as Kasparov puts it in a superb review in the February 2010 "The New York Review of Books":

"... a book that achieves its goal of being an overview of overviews, if little else. The history of the study of brain function is covered in the first chapter, tempting the reader to skip ahead. You might recall axons and dendrites from high school biology class. We also learn about cholinergic and aminergic systems and many other things that are not found by my computer's artificially intelligent English spell-checking system--or referenced again by the author. Then it's on to similarly concise, if inconclusive, surveys of artificial intelligence, chess computers, and how humans play chess."

As a one time chess enthusiast who has become a patzer with almost no competitive spirit but a love for solving chess puzzles, the book has great appeal. I found it fascinating to see how different approaches have been used first to solve the problem of playing chess by computer, and second by the author's suggestions for how those solutions might be applied to other applications of A1.

The author makes that point in these words; ches offers "an unparalleled laboratory, since both the learning process and the degree of ability obtained can be objectified and quantified, providing an excellent comparative framework on which to use rigorous analytical techniques." It was very interesting for me as a general reader in A1 with a rudimentary knowledge of chess to learn about some of those laboratory results.

Reading the book and Kasparov's fine review (see first Comment) together presents general principles and specific examples that enrich the learning experience. For example,

"In what Rasskin-Gutman explains as Moravec's Paradox, in chess, as in so many things, what computers are good at is where humans are weak, and vice versa. This gave me an idea for an experiment. What if instead of human versus machine we played as partners? My brainchild saw the light of day in a match in 1998 in León, Spain, and we called it "Advanced Chess." Each player had a PC at hand running the chess software of his choice during the game. The idea was to create the highest level of chess ever played, a synthesis of the best of man and machine."

The book, then, comes down to a matter of taste. For A. Menon, the author did not draw out the connections well enough; for me, the "ideas, elements, facts, and surprises" were enough to earn four stars. Your own mileage, of course, is almost certain to vary.


Robert C. Ross2010

Good overview of subjects but not integrated
I bought this book with the expectation of reading something on the mind, AI and chess and ways in which they overlap.In particular how the study of chess might have impacted AI research and how chess programming uses ideas from cognitive analysis of chess players.This book was not about those things...

The book starts out with an overview of the brain physiology.It assumes no prior knowledge and goes through some of the evolutionary differences between humans and other species. It discusses the basics of neurons, how they communicate and some of the ideas behind how the brain might work.In particular some of the original neural network ideas and topological maps of mind and body.The book then moves into the mind and brings up cartesian dualism.It discusses memory, emotions, what makes us self concious.It is a mixture of psychology, neuroscience and philosophy.THere has been little discussion of chess up to this point other than some vague metaphors here and there.They might be accurate but they dont follow naturally and they definately dont evolve from the text, they are sort of forced in.The book then goes to discuss computation by computers and cognitive processes, some of the differences and similarities.Nothing too deep, just overviews and the difference between methods, particularly chip deductiveness vs human inductiveness.Memory recognition, patter regocnition.The author is building up the blocks to eventually discuss how excellent chess players seem to be able to compute so quickly, which is attributed to pattern recognition rather than superior backwards induction.This is backed up through scientific experiments

The book moves into chess more completely, discussing the history first.It then gets vague again discussing the beauty of chess and the intuition that is seen as beautiful when moves are made with incomplete information inferring the human insight and feel based on implicit pattern recognition.The book gives some quick overview of the expectation of a game in terms of duration and the work of de groot who did work with great players under observation recording the neuroscience results.The parts of the chapter dont fall together that well, some statements are made which are stretched about chess composition is equivalent to theorem proving (which its not unless considering symbol manipulation theorems).The final chapter goes into the composition of chess programs.The general ideas behind them, considering chess a 0 sum game, the values of players, the backwards induction, optimized induction based on database of postions.This is the first, AI/chess that is in the book, unfortunately it really doesnt get very deep.It describes briefly the programming intent to play moves based on maximization of some n variable analysis.

I bought the book hoping for a treatise on chess, how it has illuminated techniques to study in AI, how chess programming has incorporated aspects of our cognitive processes and the future of chess programming.Only in the vaguest sense that chess programming uses database retrieval and chess masters have vast knowlege on games and positions is there a correspondence.This book serves as a good overview of how the brain works and it has a lot of information on chess.It does not integrate them well.Its discussion of chess is also romanticized rather than quantified, which is inevetable given its complexity, but the author alludes to the beauty of a position too much for the objective audience.Properties of beauty are to be shown, not assumed.All in all, i liked reading parts of the book, they introduced some interesting concepts and some topics I would like to read further on, but I dont think this book really achieves its goals. ... Read more

Product Description
"Ming Design II is a welcome update of its predecessor, itself auseful compendium on the philosophy of cognitive science. This newvolume retains the intellectual foundations, and some discussions ofclassical AI built on them, while adding connectionism, situated AI, anddynamic systems theory as extra storeys. Which of these is the moststable, and whether the foundations need to be re-worked, are questionsreaders will be eager to explore." -- Margaret A. Boden, Professor ofPhilosophy and Psychology, University of Sussex, UK "Haugeland's MindDesign II brings together nearly all the essential philosophicalperspectives in Cognitive Science. If you want to understand currentopinion on the philosophy of mind, you should make sure you are familiarwith the contents of this book." -- James L. McClelland, Carnegie MellonUniversity and the Center for the Neural Basis of Cognition

Mind design is the endeavor to understand mind (thinking, intellect) interms of its design (how it is built, how it works). Unlike traditionalempirical psychology, it is more oriented toward the "how" than the"what." An experiment in mind design is more likely to be an attempt tobuild something and make it work--as in artificial intelligence--than toobserve or analyze what already exists. Mind design is psychology byreverse engineering. When Mind Design was first published in1981, it became a classic in the then-nascent fields of cognitivescience and AI. This second edition retains four landmark essays fromthe first, adding to them one earlier milestone (Turing's "ComputingMachinery and Intelligence") and eleven more recent articles aboutconnectionism, dynamical systems, and symbolic versus nonsymbolicmodels. The contributors are divided about evenly between philosophersand scientists. Yet all are "philosophical" in that they addressfundamental issues and concepts; and all are "scientific" in that theyare technically sophisticated and concerned with concrete empiricalresearch. Contributors: Rodney A. Brooks, Paul M. Churchland, AndyClark, Daniel C. Dennett, Hubert L. Dreyfus, Jerry A. Fodor, JosephGaron, John Haugeland, Marvin Minsky, Allen Newell, Zenon W. Pylyshyn,William Ramsey, Jay F. Rosenberg, David E. Rumelhart, John R. Searle,Herbert A. Simon, Paul Smolensky, Stephen Stich, A. M. Turing, Timothyvan Gelder ... Read more

Customer Reviews (2)

Great Essays on A.I.
Mind Design II was my first serious introduction to artificial intelligence and the issues surrounding work in this multi-disciplinary area.I found it both accessible and enlightening.That being said, it is by no means a completely light read for newcomers, and it is important to invest time into thinking about the key discussion points of the book (connectionism (NFAI) vs. GOFAI, symbolism, representation, etc.).My only complaint with the book is that it is hard to tell the difference between what is current and what isn't (Turing's essay, for instance), and the fact that it was published in 1997 doesn't make it any easier.Nevertheless, I highly recommend this book to anyone interested in learning more about the philosophy and science of "mind design."

The best compendium of papers on artificial intelligence
This is the best compendium of papers in artificial intelligence that I've seen (at least on the same level of "the artificial intelligence debate" -- which is also excellent).

However, some of these ideasare getting outdated. If you want to see some true innovation in AI youshould check out Douglas Hofstadter's Fluid Concepts and CreativeAnalogies. ... Read more

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

Customer Reviews (1)

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

Product Description
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

Product Description

Traditional methods for creating intelligent computational systems have
privileged private "internal" cognitive and computational processes. In
contrast, Swarm Intelligence argues that human
intelligence derives from the interactions of individuals in a social world
and further, that this model of intelligence can be effectively applied to
artificially intelligent systems. The authors first present the foundations of
this new approach through an extensive review of the critical literature in
social psychology, cognitive science, and evolutionary computation. They
then show in detail how these theories and models apply to a new
computational intelligence methodology-particle swarms-which focuses
on adaptation as the key behavior of intelligent systems. Drilling down
still further, the authors describe the practical benefits of applying particle
swarm optimization to a range of engineering problems. Developed by
the authors, this algorithm is an extension of cellular automata and
provides a powerful optimization, learning, and problem solving method.

This important book presents valuable new insights by exploring the
boundaries shared by cognitive science, social psychology, artificial life,
artificial intelligence, and evolutionary computation and by applying these
insights to the solving of difficult engineering problems. Researchers and
graduate students in any of these disciplines will find the material
intriguing, provocative, and revealing as will the curious and savvy
computing professional.

* Places particle swarms within the larger context of intelligent
adaptive behavior and evolutionary computation.
* Describes recent results of experiments with the particle swarm
optimization (PSO) algorithm
* Includes a basic overview of statistics to ensure readers can
properly analyze the results of their own experiments using the
algorithm.
* Support software which can be downloaded from the publishers
website, includes a Java PSO applet, C and Visual Basic source
code. ... Read more

Customer Reviews (12)

I should need a little bit more
I have read the book at a stretch. In my view, the thesis (human is social) is a very simple, wit, and sound one. Maybe it is a very profound concern to us, whose complete consequences have not yet been taken in a serious consideration. Foundations and arguments in favor, coming from social and computer sciences, are orderly and properly unfolded along the book. The associated web page is being a very valuable resource for me. In short, I have made acquaintance with a good, satisfying mate. There is only a but for me: It has not moved me (but that is my but, not its...)

Good, but could have been more concise.
Swarm intelligence is burdened with an awful lot of material that is not core to PSO.A great deal of the book consists of the philosophical ramblings of the authors, rather than technical treatment of the topic at hand.An even larger chunk of the book was devoted to what was essentially a survey of AI: neural nets, evolutionary programming, heuristics, etc.Much too much space was devoted to grounding the reader in AI before proceeding.I must admit, however, that, while I found it out of place, the 'AI primer' part of the book is one of the most useful and lucid I have seen;I just think that it should have been a separate book (and this one should have been much thinner).The material that is specific to PSO is a very small fraction of the book, but is thorough and accessible;there really are few alternatives if one is particularly interested in PSO.However, if you are just interested in emergent behavior, and its applications to AI, take a look at Ant Colony Optimization (Dorigo).It covers ACO, rather than PSO, but is more more readable, and provides a much better technical treatment of the topic, if you want to avoid the philosophy and primer.

Interesting Paradigm
It's an immersive and powerful piece of scientific metrics and theoretical paradigm presentation. It shows that life can be a much deeper form of existence. The book presents the complexities of PSO in its network relativity but can be created using simple algorithms. The basis comes from the behavioural science andsocial patterns of insects such as bees and ants. Their process of colonial interaction and food foraging can be applied as a strong mathematical structure to computational science, robotics, and network technology. At the same time, you can take the exact principles -- in its raw idea -- and apply it to economic structure and business dynamics. I love how this book harks back to the parable of the blind men trying to explain what an elephant is like.

Needs more details, but a good introduction.
The authors of this book state therein that "mind is not found in covert, private chambers hidden away inside the individual, but exists out in the open; it is a public phenomenon." This would be a very difficult claim to prove from a scientific standpoint, requiring an understanding of neuroscience, consciousness, and psychology that is not yet available. The author's intent though is more modest: they want to use this statement, which they encapsulate as "swarm intelligence", as a guide to finding successful optimization algorithms. They spend many pages discussing the foundations and background behind their approach, perhaps in too much detail given the usual pragmatism exhibited by many who study algorithms. Swarm intelligence is a relatively new paradigm in the field of optimization, but its justification should come from the results it gives in practical optimization problems, not in the broad philosophical language that predominates the first part of the book.

Particle swarm optimization is introduced in the book in both 'binary' and 'real-valued' form. The authors identify three principles behind the workings of particle swarms, namely the tendency to "evaluate"; the use of comparisons to others as a way of measuring individual status or progress; and the use of imitation. These three principles they say allow individuals to adapt to highly complex environments and solve very difficult problems. A binary decision model is used to introduce binary swarm algorithm, which is given in pseudocode, and is tested using a binary-coded version of the De Jong suite of test problems for optimization algorithms. A particle swarm model over the real numbers is then discussed, along with pseudocode, Both the binary and real models of particle swarms illustrate the fact that particle swarm optimization is a consequence of social interaction. The particles or "individuals" in the swarm learn from each other, and move to become more similar to their neighbors based on the knowledge obtained. Particle swarm optimization is dependent on the existence of social structure, the latter of which is determined by the formation of neighborhoods. These neighborhoods can have a different topology, determined solely by the numerical indices assigned to each individual.

The pseudocode given for particle swarm optimization illustrates well the basic workings of the algorithm in terms of the "local" and "global" viewpoint of the particles in the swarm. First the swarm is initialized and the performance of each particle is evaluated using its current position. The performance of each individual is then compared to its best performance so far, and the velocity for each particle changed according to a formula dependent on a system parameter. Each particle is then moved to a new position and the entire process repeated until convergence is attained. When a particle is very far from its best solution previously found, the change in velocity will be greater in order to return the particle toward its best solution. The system parameter will govern how much the particle trajectories oscillate, with smaller values of this parameter ensuring smoother trajectories. The authors give examples with graphs to illustrate this behavior and the influence of the system parameter.

Being aware that particle swarm optimization is typically viewed as a kind of evolutionary algorithm, the author address in some detail the reasons for this classification and its justification. Acknowledging that particle swarm algorithms have been influenced by evolutionary computation, they discuss some of the differences between the two approaches. In evolutionary algorithms individuals survive according to their fitness, whereas in particle swarms every individual will survive. In addition, in particle swarms, it is the velocities that are adjusted, whereas in evolutionary computing it is the positions that are state. The authors express this by saying that it is the "fate" rather than the "state" that is altered in particle swarm optimization.

The authors include an entire chapter on applications in the book, one of them being the use of particle swarms to evolve neural networks. Evolved neural networks have been shown to perform better in some cases than ones designed from scratch. After discussing some of the approaches to evolving neural networks, the authors point out, correctly, that hardly any of the studies in evolving neural networks are quantitative studies of how well they perform relative to other approaches Performance metrics are hardly ever given, which would allow interested parties to make objective and intelligent decisions on which approach is the most viable. The author's approach of using particle swarms to evolve neural networks also, interestingly, involves evolving the transfer functions of the neural networks, and they test their approach by using the Iris Data Set, a frequent benchmark for classification algorithms. Preliminary results indicate that their approach is a viable one and that it shows promise, but they admit that further experiments are needed in order to form valid conclusions.

So are the optimization algorithms based on swarm intelligence better than those that are based on, for example, on evolutionary algorithms? Are they better than those that are purely randomized algorithms? The authors are not shy about discussing how swarm intelligence optimization algorithms compare with other optimization algorithms, particularly randomized algorithms and the now famous "free-lunch" theorems of David Wolpert and William Macready. They discuss the free-lunch theorems via a very interesting example dealing with finding one's way out of a room. Using this example, they are convincing in their claim that even though no algorithm can be said to be better than any other when averaged over all cost functions, this averaging is done over processes or tasks that might be deemed absurd in the context of many problems of practical interest. Thus for "real" problems, one algorithm might indeed be "better" than another.

Mind is Social
My original motivation for reading Swarm Intelligence was a desire to learn about the Particle Swarm Optimization (PSO) algorithm -- in particular, to learn how to implement it in a Java program. To the credit of its authors, what I found in Swarm Intelligence was far more than that. The authors have taken on the rather daunting task of presenting a new paradigm -- a new way of thinking about mind and intelligence -- and they have succeeded.

PSO, itself, is deceptively simple. The heart of the algorithm can be written in a single line of code. Understanding the basis for its approach to intelligence isn't difficult, either. The authors begin their explanation using the old parable about the blind men and the elephant. You are most likely familiar with the story. In summary form, it is about a group of blind men standing around an elephant each declaring "what an elephant is like" based upon which part of the elephant they are touching -- and elephant is like: a wall (side); a tree trunk (leg); a hose (trunk); a fan (ear); and so on.

What is wrong with this story, the authors point out, is its implicit assumption that these blind men are also deaf. If not, as they each announced their impressions the individuals, as a group, would discover much more about what an elephant is. The significance here is easily missed. The capabilities of a group emerge from the individuals immersed in it. The group can do more (see more, discover more, experiment more) than the individuals from which it emerges and, by virtue of their immersion in it, the individuals benefit (and in turn, the group then benefits as it now emerges from these "benefited" individuals).

The authors view this emergent/immergent "cycle" as the driving force behind mind and intelligence. In contrast to the normal (phenomenological) view of mind as an internal, private "thing that thinks," the authors assert that mind is something requiring sociality. To put it bluntly (and the authors do), in the absence of social immersion there is no mind; mind is social. The majority of the book is focused on this: why it's true, how it's true and how it is implemented in the PSO algorithm.

It is easy to see how the book might have ended up a long philosophical argument. It isn't. Instead, the authors present a nicely written history of efforts to achieve "computational intelligence" (a much better phrase than the more familiar "artificial intelligence") including great summaries of evolutionary approaches, fuzzy logic, neural nets and artificial life. Along the way they point out recent advances in psychology and sociology. The net effect is that they don't need to argue their point. By the end of this part of the book the importance of sociality has become rather obvious. If you are interested in sociology, psychology, engineering and/or computer science you will enjoy this part of the book immensely, learn a lot and find a wealth of references to additional sources of information.

The second part of the book presents the PSO algorithm, compares its performance with other methodologies (in addition to being simpler to understand and implement, it's an order of magnitude faster when applied to certain problems -- training neural nets, for example), demonstrates how it is applied to some "real life" problems and discusses some implications of (and speculations about) the approach. As with the first part of the book, the presentation is clear, concise and informative. There is, though, indications here that the PSO approach is rather new (young). There isn't enough experience with PSO yet to give this part of the book the same feeling of depth one gets from the first part.

It's worth noting that the presentation (and description) of the PSO algorithm is done in mathematical terms. I would have much preferred a programming approach (using pseudo code) not because the math is too difficult (it's not) but because I haven't been "immersed in a mathematically minded social group" for many years. The almost exclusive use of Greek letters for symbols (variables) made reading difficult. Not only are they visually unfamiliar, I don't know their pronunciations (to illustrate the difficulty by way of analogy, consider the difference between reading "y equals b times x plus z" and "xgt equals kqj times yxf plus ktv"). I ended up rewriting the formulas in more familiar terms (using the text to figure out what the symbols represent when necessary) before I felt that I understood them.

Mentioning my problem with the math is not meant to criticize but to suggest that the book could have been made accessible to more people had it also contained a more readable (and retainable) form of the algorithm, perhaps in an appendix. A good analogy of the PSO approach (more detailed than the "blind men" story) would also have been helpful. The only real criticism I have of the book's content is a minor one. Being as it is focused on the social requirements for mind, it tends to overlook the degree of individuality required to make PSO work. The algorithm, itself, has variables which control the expression of individuality and without which it could not work (at least not well), but this flipside to the social nature of the algorithm is never discussed as such. PSO works well precisely because it maintains the rather chaotic balance between the effects of sociality and individuality. The book presents a rather one-sided view of this balance.

An aside for programmers: There is a companion site (of sorts) on the web for the book through which you can download Visual Basic and C source code of PSO implementations. There is also a Java applet which demonstrates PSO applied to a number of test functions but the source code for it is not available. There will also be an open source Java implementation as soon as I can make one available. ... Read more

Product Description
"This book is ... not on philosophy, science or reality, but why and how minds might invent such things. The laws of mind, not the laws of gravity or electricity, but the methods of the mind that made these tools. It is not on brains of neurons, controllers or computers, but the logical possibilities of mind, from minimal axioms deducing all the kinds of mind that are and can ever be."Mind Making applies original artificial intelligence research to precisely define "mind". On that foundation, author Patrick Roberts advances ancient questions of free-will, reality and ethics. Deeper, the book offers a scientific method for resolving philosophical questions."How to prove a model of mind? Only by testing an analogous combination of entirely mindless parts. Otherwise, you remain trapped in endless debates, never reaching certainties because you can't suspend your own mind. Twenty-five hundred years of futile verbal philosophical debate ends. Philosophy becomes an engineering problem: Machine mind m outperformed mind n in a statistically significant set of tests. n's assumptions about reality are wrong. m's are right and are complete because m contains no minds but those we made."To the psychologist, Mind Making offers a model of the human mind unburdened by the technicalities of neurons and chemistry. To the engineer, designs for more reliable, powerful machines. To the philosopher, proven ultimate reality. To the lay reader, better knowledge of his mind, and of his world as an effect of that mind."These laws of mind are all that can be true for everyone, everywhere, forever. They can't be false because they made truth. Always true, you need never doubt them. In your mind, they are the last possessions you can lose. By comparison, all other knowledge is trivia." ... Read more

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Simply. Brilliant!
Mind Making is a one-of-a-kind piece of work that avoids the usual traps of other philosophy and artificial intelligence books. Patrick Roberts' Mind Making is a dense read with many, many superb ideas. You don't have to be a philosopher or computer programmer to appreciate it. And, at just over 150 pages, it makes for a quick, yet dense but satisfying read. This is the kind of book that will hold people's interest for many years - make that decades - to come. My only gripe: the book feels somehow incomplete. The book's ambition doesn't fit its length. It's as if the author left out some of the pieces of the puzzle. Though I'm sure we can look forward to a revised edition in the future which will contain solutions to the unanswered questions. Overall, Mind Making is a definite must-read! ... Read more

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Creativity is one of the least understood aspects ofintelligence and is often seen as `intuitive' and not susceptible torational enquiry. Recently, however, there has been a resurgence ofinterest in the area, principally in artificial intelligence andcognitive science, but also in psychology, philosophy, computerscience, logic, mathematics, sociology, and architecture and design.This volume brings this work together and provides an overview of thisrapidly developing field. It addresses a range of issues. Cancomputers be creative? Can they help us to understand humancreativity? How can artificial intelligence (AI) enhance humancreativity? How, in particular, can it contribute to the `sciences ofthe artificial', such as design? Does the new wave of AI(connectionism, geneticism and artificial life) offer more promise inthese areas than classical, symbol-handling AI? What would theimplications be for AI and cognitive science if computers couldnot be creative?
These issues are explored in five interrelated parts, each of which isintroducted and explained by a leading figure in the field.
- Prologue (Margaret Boden)
- Part I: Foundational Issues (Terry Dartnall)
- Part II: Creativity and Cognition (Graeme S. Halford andRobert Levinson)
- Part III: Creativity and Connectionism (Chris Thornton)
- Part IV: Creativity and Design (John Gero)
- Part V: Human Creativity Enhancement (Ernest Edmonds)
- Epilogue (Douglas Hofstadter)
For researchers in AI, cognitive science, computer science,philosophy, psychology, mathematics, logic, sociology, andarchitecture and design; and anyone interested in the rapidly growingfield of artificial intelligence and creativity.
... Read more

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An inadequate infrastructure for software testing is causing major losses to the world economy. The characteristics of software quality problems are quite similar to other tasks successfully tackled by artificial intelligence techniques. The aims of this book are to present state-of-the-art applications of artificial intelligence and data mining methods to quality assurance of complex software systems, and to encourage further research in this important and challenging area. ... Read more

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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