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Machine Learning and Adaptive Computation

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Support Vector Machines, Neural Networks, and Fuzzy Logic Models

This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.

Downloadable instructor resources available for this title: solution manual

This text covers all the material needed to understand the principles behind the AI approach to robotics and to program an artificially intelligent robot for applications involving sensing, navigation, planning, and uncertainty. Robin Murphy is extremely effective at combining theoretical and practical rigor with a light narrative touch. In the overview, for example, she touches upon anthropomorphic robots from classic films and science fiction stories before delving into the nuts and bolts of organizing intelligence in robots.Following the overview, Murphy contrasts AI and engineering approaches and discusses what she calls the three paradigms of AI robotics: hierarchical, reactive, and hybrid deliberative/reactive. Later chapters explore multiagent scenarios, navigation and path-planning for mobile robots, and the basics of computer vision and range sensing. Each chapter includes objectives, review questions, and exercises. Many chapters contain one or more case studies showing how the concepts were implemented on real robots. Murphy, who is well known for her classroom teaching, conveys the intellectual adventure of mastering complex theoretical and technical material.An Instructor's Manual including slides, solutions, sample tests, and programming assignments is available to qualified professors who are considering using the book or who are using the book for class use.

Downloadable instructor resources available for this title: instructor's manual

foreword by Michael Arbib


This introduction to the principles, design, and practice of intelligent behavior-based autonomous robotic systems is the first true survey of this robotics field. The author presents the tools and techniques central to the development of this class of systems in a clear and thorough manner. Following a discussion of the relevant biological and psychological models of behavior, he covers the use of knowledge and learning in autonomous robots, behavior-based and hybrid robot architectures, modular perception, robot colonies, and future trends in robot intelligence.

The text throughout refers to actual implemented robots and includes many pictures and descriptions of hardware, making it clear that these are not abstract simulations, but real machines capable of perception, cognition, and action.


An Introduction

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.

The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

Downloadable instructor resources available for this title: solution manual

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, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.

An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text.

The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

Downloadable instructor resources available for this title: solution manual


Computers and Thought showcases the work of the scientists who not only defined the field of Artificial Intelligence, but who are responsible for having developed it into what it is today. Originally published in 1963, this collection includes twenty classic papers by such pioneers as A. M. Turing and Marvin Minsky who were behind the pivotal advances in artificially simulating human thought processes with computers.

Among the now hard-to-find articles are reports of computer programs that play chess and checkers, prove theorems in logic and geometry, solve problems in calculus, balance assembly lines, recognize visual temporal patterns, and communicate in natural language. The reports of simulation of cognitive processes include computer models of human behavior in logic problems, deciding on common stock portfolios, and carrying out social interaction. Models of verbal learning behavior, predictive behavior in two-choice experiments, and concept formation are also included.

Articles by: Paul Armer. Carol Chomsky. Geoffrey P. E. Clarkson. Edward A. Feigenbaum. Julian Feldman. H. Gelernter. Bert F. Green, Jr. John T. Gullahorn. Jeanne E. Gullahorn. J. R. Hansen. Carl I. Hovland. Earl B. Hunt. Kenneth Laughery. Robert K. Lindsay. D. W. Loveland. Marvin Minsky. Ulric Neisser. Allen Newell. A. L. Samuel. Oliver G. Selfridge. J. C. Shaw. Herbert A. Simon. James R. Slagle. Fred M. Tonge. A. M. Turing. Leonard Uhr. Charles Vossler. Alice K. Wolf.


This book presents a coherent approach to the fast moving field of machine vision, using a consistent notation based on a detailed understanding of the image formation process. It covers even the most recent research and will provide a useful and current reference for professionals working in the fields of machine vision, image processing, and pattern recognition.An outgrowth of the author's course at MIT, Robot Vision presents a solid framework for understanding existing work and planning future research. Its coverage includes a great deal of material that important to engineers applying machine vision methods in the real world. The chapters on binary image processing, for example, help explain and suggest how to improve the many commercial devices now available. And the material on photometric stereo and the extended Gaussian image points the way to what may be the next thrust in commercialization of the results in this area. The many exercises complement and extend the material in the text, and an extensive bibliography will serve as a useful guide to current research.Contents: Image Formation and Image Sensing. Binary Images: Geometrical Properties; Topological Properties. Regions and Image Segmentation. Image Processing: Continuous Images; Discrete Images. Edges and Edge Finding. Lightness and Color. Reflectance Map: Photometric Stereo Reflectance Map; Shape from Shading. Motion Field and Optical Flow. Photogrammetry and Stereo. Pattern Classification. Polyhedral Objects. Extended Gaussian Images. Passive Navigation and Structure from Motion. Picking Parts out of a Bin.Berthold Klaus Paul Horn is Associate Professor, Department of Electrical Engineering and Computer Science, MIT. Robot Vision is included in the MIT Electrical Engineering and Computer Science Series.

Downloadable instructor resources available for this title: solution manual

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