Evolutionary Computation: Toward a New Philosophy of Machine Intelligence: 1 (IEEE Press Series on Computational Intelligence) - Hardcover

Fogel, David B.

Hardcover

ISBN 10: 0471669512 ISBN 13: 9780471669517

Verlag: Wiley-Interscience, 2006

Inhaltsangabe

This Third Edition provides the latest tools and techniques that enable computers to learn

The Third Edition of this internationally acclaimed publication provides the latest theory and techniques for using simulated evolution to achieve machine intelligence. As a leading advocate for evolutionary computation, the author has successfully challenged the traditional notion of artificial intelligence, which essentially programs human knowledge fact by fact, but does not have the capacity to learn or adapt as evolutionary computation does.

Readers gain an understanding of the history of evolutionary computation, which provides a foundation for the author's thorough presentation of the latest theories shaping current research. Balancing theory with practice, the author provides readers with the skills they need to apply evolutionary algorithms that can solve many of today's intransigent problems by adapting to new challenges and learning from experience. Several examples are provided that demonstrate how these evolutionary algorithms learn to solve problems. In particular, the author provides a detailed example of how an algorithm is used to evolve strategies for playing chess and checkers.

As readers progress through the publication, they gain an increasing appreciation and understanding of the relationship between learning and intelligence. Readers familiar with the previous editions will discover much new and revised material that brings the publication thoroughly up to date with the latest research, including the latest theories and empirical properties of evolutionary computation.

The Third Edition also features new knowledge-building aids. Readers will find a host of new and revised examples. New questions at the end of each chapter enable readers to test their knowledge. Intriguing assignments that prepare readers to manage challenges in industry and research have been added to the end of each chapter as well.

This is a must-have reference for professionals in computer and electrical engineering; it provides them with the very latest techniques and applications in machine intelligence. With its question sets and assignments, the publication is also recommended as a graduate-level textbook.

Über diesen Titel

Über die Autorin bzw. den Autor

David B. Fogel is chief executive officer of Natural Selection, Inc. in La Jolla, CA―a small business focused on solving difficult problems in industry, medicine, and defense using evolutionary computation, neural networks, fuzzy systems, and other methods of computational intelligence. Dr. Fogel’s experience in evolutionary computation spans 20 years and includes applications in pharmaceutical design, computer-assisted mammography, data mining, factory scheduling, financial forecasting, traffic flow optimization, agent-based adaptive combat systems, and many other areas. Prior to cofounding Natural Selection, Inc. in 1993, Dr. Fogel was a systems analyst at Titan Systems, Inc. (1984–1988), and a senior principal engineer at ORINCON Corporation (1988–1993).
Dr. Fogel received his Ph.D. degree in engineering sciences (systems science) from the University of California at San Diego (UCSD) in 1992. He earned an M.S. degree in engineering sciences (systems science) from UCSD in 1990, and a B.S. in mathematical sciences (probability and statistics) from the University of California at Santa Barbara in 1985. He has taught university courses at the graduate and undergraduate level in stochastic processes, probability and statistics, and evolutionary computation. Dr. Fogel is a prolific author in evolutionary computation, having published over 50 journal papers, as well as 100 conference publications, 20 contributions in book chapters, two videos, four computer games, and six books―most recently, Blondie24: Playing at the Edge of AI (Morgan Kaufmann, 2002). In addition, Dr. Fogel is coeditor in chief of the Handbook of Evolutionary Computation (Oxford, 1997) and was the founding editor-in-chief of the IEEE Transactions on Evolutionary Computation (1996–2002). He serves as editor-in-chief for the journal BioSystems and is a member of the editorial board of several other international technical journals.
Dr. Fogel served as a Visiting Fellow of the Australian Defence Force Academy in November 1997, and is a member of many professional societies including the American Association for the Advancement of Science, the American Association for Artificial Intelligence, Sigma Xi, and the New York Academy of Sciences. He was the founding president of the Evolutionary Programming Society in 1991 and is a Fellow of the IEEE, as well as an associate member of the Center for the Study of Evolution and the Origin of Life (CSEOL) at the University of California at Los Angeles. Dr. Fogel is a frequently invited lecturer at international conferences and a guest for television and radio broadcasts. His honors and awards include the 2001 Sigma Xi Southwest Region Young Investigator Award, the 2003 Sigma Xi San Diego Section Distinguished Scientist Award, the 2003 SPIE Computational Intelligence Pioneer Award, and the 2004 IEEE Kiyo Tomiyasu Technical Field Award.

Von der hinteren Coverseite

This Third Edition provides the latest tools and techniques that enable computers to learn

The Third Edition of this internationally acclaimed publication provides the latest theory and techniques for using simulated evolution to achieve machine intelligence. As a leading advocate for evolutionary computation, the author has successfully challenged the traditional notion of artificial intelligence, which essentially programs human knowledge fact by fact, but does not have the capacity to learn or adapt as evolutionary computation does.

Readers gain an understanding of the history of evolutionary computation, which provides a foundation for the author's thorough presentation of the latest theories shaping current research. Balancing theory with practice, the author provides readers with the skills they need to apply evolutionary algorithms that can solve many of today's intransigent problems by adapting to new challenges and learning from experience. Several examples are provided that demonstrate how these evolutionary algorithms learn to solve problems. In particular, the author provides a detailed example of how an algorithm is used to evolve strategies for playing chess and checkers.

As readers progress through the publication, they gain an increasing appreciation and understanding of the relationship between learning and intelligence. Readers familiar with the previous editions will discover much new and revised material that brings the publication thoroughly up to date with the latest research, including the latest theories and empirical properties of evolutionary computation.

The Third Edition also features new knowledge-building aids. Readers will find a host of new and revised examples. New questions at the end of each chapter enable readers to test their knowledge. Intriguing assignments that prepare readers to manage challenges in industry and research have been added to the end of each chapter as well.

This is a must-have reference for professionals in computer and electrical engineering; it provides them with the very latest techniques and applications in machine intelligence. With its question sets and assignments, the publication is also recommended as a graduate-level textbook.

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Evolutionary Computation

Toward a New Philosophy of Machine IntelligenceBy David B. Fogel

John Wiley & Sons

Copyright © 2006 The Institute of Electrical and Electronics Engineers, Inc.
All right reserved.
ISBN: 978-0-471-66951-7

Chapter One

DEFINING ARTIFICIAL INTELLIGENCE

1.1 BACKGROUND

Calculators are not intelligent. Calculators give the right answers to challenging math problems, but everything they "know" is preprogrammed by people. They can never learn anything new, and outside of their limited domain of utility, they have the expertise of a stone. Calculators are able to solve problems entirely because people are already able to solve those same problems.

Since the earliest days of computing, we have envisioned machines that could go beyond our own ability to solve problems-intelligent machines. We have generated many computing devices that can solve mathematical problems of enormous complexity, but mainly these too are merely "calculators." They are preprogrammed to do exactly what we want them to do. They accept input and generate the correct output. They may do it at blazingly fast speeds, but their underlying mechanisms depend on humans having already worked out how to write the programs that control their behavior. The dream of the intelligent machine is the vision of creating something that does not depend on having people preprogram its problem-solving behavior. Put another way, artificial intelligence should not seek to merely solve problems, but should rather seek to solve the problem of how to solve problems.

Although most scientific disciplines, such as mathematics, physics, chemistry, and biology, are well defined, the field of artificial intelligence (AI) remains enigmatic. This is nothing new. Even 20 years ago, Hofstadter (1985, p. 633) remarked, "The central problem of AI is the question: What is the letter 'a'? Donald Knuth, on hearing me make this claim once, appended, 'And what is the letter 'i'?'-an amendment that I gladly accept." Despite nearly 50 years of research in the field, there is still no widely accepted definition of artificial intelligence. Even more, a discipline of computational intelligence-including research in neural networks, fuzzy systems, and evolutionary computation-has gained prominence as an alternative to AI, mainly because AI has failed to live up to its promises and because many believe that the methods that have been adopted under the old rubric of AI will never succeed.

It may be astonishing to find that five decades of research in artificial intelligence have been pursued without fundamentally accepted goals, or even a simple but rigorous definition of the field itself. Even today, it is not uncommon to hear someone offer, in a formal lecture, that artificial intelligence is difficult to define, followed by absolutely no attempt to define it, followed by some interesting research on a problem for which a better solution has been found by some method that is then deemed to be artificially intelligent.

When definitions have been offered, they have often left much to be desired. Intelligent machines may manipulate symbols to solve problems, but simple symbol manipulation cannot be the basis for a broadly useful definition of artificial intelligence (cf., Buchanan and Shortliffe, 1985, p. 3). All computers manipulate symbols; at the most rudimentary level these are ones and zeroes. It is possible for people to assign meaning to these ones and zeroes, and combinations of ones and zeroes, but then where is the intelligence? There is no fundamental difference between a person assigning meaning to symbols in a computer program and a person assigning meaning to binary digits manipulated by a calculator. Neither the program nor the calculator has created any symbolic meaning on its own.

Waterman (1986, p. 10) offered that artificial intelligence was "the part of computer science concerned with developing intelligent computer programs." This tautological statement offers no basis for designing an intelligent machine or program.

Rich (1983, p. 1) offered, "Artificial intelligence (AI) is the study of how to make computers do things at which, at the moment, people are better," which was echoed even as recently as 1999 by Lenat (in Moody, 1999). But this definition, if regarded statically, precludes the very existence of artificial intelligence. Once a computer program exceeds the capabilities of a human, the program is no longer in the domain of AI.

Russell (quoted in Ubiquity, 2004) offered, "An intelligent system is one whose expected utility is the highest that can be achieved by any system with the same computational limitations." But this definition appears to offer intelligence to a calculator, for there can be no higher expected utility than getting four as the right answer to two plus two. It might even extend to a pebble, sitting at equilibrium on a bottom of a pond, with no computational ability whatsoever. It is no wonder that we have not achieved our dreams when our efforts have been defined so poorly.

The majority of definitions of artificial intelligence proffered over decades have relied on comparisons to human behavior. Staugaard (1987, p. 23) attributed a definition to Marvin Minsky-"the science of making machines do things that would require intelligence if done by men"-and suggested that some people define AI as the "mechanization, or duplication, of the human thought process." Using humans as a benchmark is a common, and I will argue misplaced, theme historically in AI. Charniak and McDermott (1985, p. 6) offered, "Artificial intelligence is the study of mental faculties through the use of computational models," while Schildt (1987, p. 11) claimed, "An intelligent program is one that exhibits behavior similar to that of a human when confronted with a similar problem. It is not necessary that the program actually solve, or attempt to solve, the problem in the same way that a human would."

What then if there were no humans? What if humans had never evolved? Would this preclude the possibility of intelligent machines? What about intelligent machines on other planets? Is this precluded because no humans reside on other planets? Humans are intelligent, but they are only one example of intelligence, which must be defined properly in order to engage in a meaningful discourse about the possibility of creating intelligent machines, be they based in silicon or carbon. I will return to this point later in this chapter.

The pressing question, "What is AI?" would become mere semantics, nothing more than word games, if only the answers did not suggest or imply radically different avenues of research, each with its own goals. Minsky (1991) wrote, "Some researchers simply want machines to do the various sorts of things that people call intelligent. Others hope to understand what enables people to do such things. Still other researchers want to simplify programming." That artificial intelligence is an extremely fragmented collection of endeavors is as true today as it was in 1991. Yet the vision of what is to be created remains prominent today, even as it did when Minsky (1991) wrote: "Why can't we build, once and for all, machines that grow and improve themselves by learning from experience? Why can't we simply explain what we want, and then let our machines do experiments or read some books or go to school, the sorts of things that people do. Our machines today do no such things."

The disappointing reality is that, actually, even in 1991 machines did indeed do many of these things and the methods that allowed these machines to achieve these results have a long history. What is more disappointing is that this history is mostly unknown by those who work in what they describe as "artificial intelligence." One of the reasons that less progress has been made than was envisioned in the 1950s stems from a general lack of awareness of the progress that has in fact been made, a symptom that is characteristic of new fields and particularly of AI research. This text seeks to provide a focused explication of particular methods that indeed allow machines to improve themselves by learning from experience and to explain the fundamental theoretical and practical considerations of applying them to problems of machine learning. To begin this explication, the discussion first goes back to the Turing Test.

1.2 THE TURING TEST

Turing (1950) considered the question, "Can machines think?" Rather than define the terms "machines" or "think," Turing proposed a test that begins with three people: a man (A), a woman (B), and an interrogator (C). The interrogator is to be separated from both A and B, say, in a closed room (Figure 1-1) but may ask questions of both A and B. The interrogator's objective is to determine which (A or B) is the woman and, by consequence, which is the man. It is A's objective to cause C to make an incorrect identification. Turing provided the following example of a question posed to the man:

"C: Will X [C's name for A] please tell me the length of his or her hair?"

"A: My hair is shingled, and the longest strands are about nine inches long."

Player A may be deceitful, if he so desires. In contrast, the object for B is to help the interrogator. Turing suggested that the probable best strategy for her is to give truthful answers. In order that the pitch of the voice or other external clues may not aid in C's decision, a teleprinter was to be used for communication between the rooms.

Turing then replaced the original question, "Can machines think?" with the following: "We now ask the question, 'What will happen when a machine takes the part of A in this game?' Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman." This question separates the physical and intellectual capabilities of humans. The form of interrogation prevents C from using sensory information regarding A's or B's physical characteristics. Presumably, if the interrogator were able to show no increased ability to decide between A and B when the machine was playing as opposed to when the man was playing, then the machine would be declared to have passed the test. Whether or not the machine should then be judged capable of thinking was left unanswered. Turing in fact dismissed this original question as being "too meaningless to deserve discussion."

There is a common misconception that the Turing Test involves a machine fooling an interrogator into believing that it is a person. Note from the above description that this is not the essence of the test. The test determines whether or not a machine can be as effective as a man in fooling an interrogator into believing that it is a woman. Since the advent of the Internet and instant messaging, we have seen that it is quite easy for a man to fool an interrogator into believing that he is a woman. Turing quite likely did not envision the challenge to be quite so great.

Turing limited the possible machines to be the set of all digital computers. He indicated through considerable analysis that these machines are universal, that is, all computable processes can be executed by such a machine. Thus, the restriction to digital computers was not a significant limitation of the test. With respect to the suitability of the test itself, Turing thought the game might be weighted "too heavily against the machine. If the man were to try to pretend to be the machine he would clearly make a very poor showing." Hofstadter (1985, pp. 514-520) related an amusing counterexample in which he was fooled temporarily in such a manner, but note that this obverse version of the Turing Test is not a proper analog because, properly, the man would have to do as well as a woman in pretending to be a machine, and then what would this test be intended to judge?

Turing (1950) considered and rejected a number of objections to the plausibility of a "thinking machine," although somewhat remarkably he felt that an argument supporting the existence of extrasensory perception in humans was the most compelling of all objections. The "Lady Lovelace" objection (Countess of Lovelace, 1842), referring to a memoir by the Countess of Lovelace on Babbage's Analytical Engine, is the most common present refutation of a thinking machine. The argument asserts that a computer can only do what it is programmed to do and, therefore, will never be capable of generating anything new. Turing countered this argument by equating it with a statement that a machine can never take us by surprise, but he noted that machines often act in unexpected ways because the entire determining set of initial conditions of the machine is generally unknown: An accurate prediction of all possible behavior of the mechanism is impossible.

Moreover, Turing suggested that a thinking machine should be a learning machine, capable of altering its own configuration through a series of rewards and punishments. Thus, it could modify its own programming and generate unexpected behavior. He speculated that "in about fifty years' time it will be possible to programme computers, with a storage capacity of about 10 [bits], to make them play the imitation game so well that an average interrogator will not have more than a 70 percent chance of making the right identification after five minutes of questioning" (Turing, 1950). It is now a few years past the time frame of Turing's prognostication and there is nothing to suggest that we are close to creating a machine that could pass his test.

1.3 SIMULATION OF HUMAN EXPERTISE

The acceptance of the Turing Test focused attention on mimicking human behavior. At the time (1950), it was beyond any reasonable consideration that a computer could pass the Turing Test. Rather than focus on imitating human behavior in conversation, attention was turned to more limited domains of interest. Simple two-person games of strategy were selected. These games received attention for at least three reasons: (1) Their rules are static, known to both players, and easy to express in a computer program; (2) they are examples from a class of problems concerned with reasoning about actions; and (3) the ability of a game-playing computer can be measured against human experts.

The majority of research in game playing has been aimed at the development of heuristics that can be applied to two-person, zero-sum, nonrandom games of perfect information (Jackson, 1985). The term zero-sum indicates that any potential gain to one player will be reflected as a corresponding loss to the other player. The term nonrandom means that the allocation and positioning of resources in the game (e.g., pieces on a chess board) is purely deterministic. Perfect information indicates that both players have complete knowledge regarding the disposition of both players' resources (e.g., tic-tac-toe, not poker).

The general protocol was to examine an expert's decisions during a game so as to discover a consistent set of parameters or questions that are evaluated during his or her decision-making process. These conditions could then be formulated in an algorithm that is capable of generating behavior that is similar to that of the expert when faced with identical situations. It was believed that if a sufficient quantity or "coverage" of heuristics could be programmed into the computer, the sheer speed and infallible computational ability of the computer would enable it to match or even exceed the ability of the human expert.

1.3.1 Samuel's Checker Program

One of the earliest efforts along these lines was offered by Samuel (1959), who wrote a computer program that learned to play checkers. Checkers was chosen for several reasons: (1) There was, and still is, no known algorithm that provides for a guaranteed win or draw; (2) the game is well defined, with an obvious goal; (3) the rules are fixed and well known; (4) there are human experts who can be consulted and against which progress of the program can be tested; and (5) the activity is familiar to many people. The general procedure of the program was to look ahead a few moves at a time and evaluate the resulting board positions.

(Continues...)

Excerpted from Evolutionary Computationby David B. Fogel Copyright © 2006 by The Institute of Electrical and Electronics Engineers, Inc.. Excerpted by permission.
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Details zur Ausgabe

• Verlag: Wiley-Interscience
• Erscheinungsdatum: 2006
• ISBN 10: 0471669512
• ISBN 13: 9780471669517
• Einband: Tapa dura
• Sprache: Englisch
• Auflage: 3
• Anzahl der Seiten: 296
• Kontakt zum Hersteller: Nicht verfügbar