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Books : Computers & Internet : Computer Science : Artificial Intelligence : Artificial Life
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During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
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"This is the best general book on Genetic Algorithms written to date. It covers background, history, and motivation; it selects important, informative examples of applications and discusses the use of Genetic Algorithms in scientific models; and it gives a good account of the status of the theory of Genetic Algorithms. Best of all the book presents its material in clear, straightforward, felicitous prose, accessible to anyone with a college-level scientific background. If you want a broad, solid understanding of Genetic Algorithms -- where they came from, what's being done with them, and where they are going -- this is the book. -- John H. Holland, Professor, Computer Science and Engineering, and Professor of Psychology, The University of Michigan; External Professor, the Santa Fe Institute.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.
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Alan Turing was one of the most influential thinkers of the 20th century. In 1935, aged 22, he developed the mathematical theory upon which all subsequent stored-program digital computers are modeled. At the outbreak of hostilities with Germany in September 1939, he joined the Goverment Codebreaking team at Bletchley Park, Buckinghamshire and played a crucial role in deciphering Engima, the code used by the German armed forces to protect their radio communications. Turing's work on the version of Enigma used by the German navy was vital to the battle for supremacy in the North Atlantic. He also contributed to the attack on the cyphers known as 'Fish,' which were used by the German High Command for the encryption of signals during the latter part of the war. His contribution helped to shorten the war in Europe by an estimated two years. After the war, his theoretical work led to the development of Britain's first computers at the National Physical Laboratory and the Royal Society Computing Machine Laboratory at Manchester University. Turing was also a founding father of modern cognitive science, theorizing that the cortex at birth is an 'unorganized machine' which through 'training' becomes organized 'into a universal machine or something like it.' He went on to develop the use of computers to model biological growth, launching the discipline now referred to as Artificial Life. The papers in this book are the key works for understanding Turing's phenomenal contribution across all these fields. The collection includes Turing's declassified wartime 'Treatise on the Enigma'; letters from Turing to Churchill and to codebreakers; lectures, papers, and broadcasts which opened up the concept of AI and its implications; and the paper which formed the genesis of the investigation of Artifical Life.
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The Self-Organizing Map (SOM), with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category. Many fields of science have adopted the SOM as a standard analytical tool: in statistics,signal processing, control theory, financial analyses, experimental physics, chemistry and medicine. A new area is organization of very large document collections. The SOM is also one of the most realistic models of the biological brain functions. This new edition includes a survey of over 2000 contemporary studies to cover the newest results; the case examples were provided with detailed formulae, illustrations and tables; a new chapter on software tools for SOM was written, other chapters were extended or reorganized.Some praise for previous editions:
"The portions of the book discussing the self-organizing map are exquisite...The book ends with an overview of the self-organizing map literature and a glossary. I have never seen a literature overview of this quality, comprehensiveness, and refinement...The result is a marvelous tool for finding just about any information that exists regarding self-organizing maps. Rarely has any subject been provided with such an index of knowledge. The glossary is excellent and covers a number of terms not discussed in the text. This glossary will be of great use to students just learning the jargon, as well as to more experienced individuals who have not had the luxury of learning the terminology gradually as it developed over the past three decades. In summary, Kohonen has created a masterpiece. I unhesitatingly recommend it. Everyone, from the most experienced researchers and practitioners to rank beginners, will benefit from this book." -IEEE Transactions on Neural Networks
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What is life? Is it just the biologically familiar--birds, trees, snails, people--or is it an infinitely complex set of patterns that a computer could simulate? What role does intelligence play in separating the organic from the inorganic, the living from the inert? Does life evolve along a predestined path, or does it suddenly emerge from what appeared lifeless and programmatic?In this easily accessible and wide-ranging survey, Claus Emmeche outlines many of the challenges and controversies involved in the dynamic and curious science of artificial life. Emmeche describes the work being done by an international network of biologists, computer scientists, and physicists who are using computers to study life as it could be, or as it might evolve under conditions different from those on earth.
Many artificial-life researchers believe that they can create new life in the computer by simulating the processes observed in traditional, biological life-forms. The flight of a flock of birds, for example, can be reproduced faithfully and in all its complexity by a relatively simple computer program that is designed to generate electronic "boids." Are these "boids" then alive? The central problem, Emmeche notes, lies in defining the salient differences between biological life and computer simulations of its processes. And yet, if we can breathe life into a computer, what might this mean for our other assumptions about what it means to be alive?
The Garden in the Machine touches on every aspect of this complex and rapidly developing discipline, including its connections to artificial intelligence, chaos theory, computational theory, and studies of emergence. Drawing on the most current work in the field, this book is a major overview of artificial life. Professionals and nonscientists alike will find it an invaluable guide to concepts and technologies that may forever change our definition of life.
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Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis.
The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models.
The book is a new edition of Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also
- provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes.
- give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge.
- give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs.
- present a thorough introduction to state-of-the-art solution and analysis algorithms.
The book is intended as a textbook, but it can also be used for self-study and as a reference book.
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The advent of powerful processing technologies and the advances in software development tools have drastically changed the approach and implementation of computational research in fundamental properties of living systems through simulating and synthesizing biological entities and processes in artificial media. Nowadays realistic physical and physiological simulation of natural and would-be creatures, worlds and societies becomes a low-cost task for ordinary home computers. The progress in technology has dramatically reshaped the structure of the software, the execution of a code, and visualization fundamentals. This has led to the emergence of novel breeds of artificial life software models, including three-dimensional programmable simulation environment, distributed discrete events platforms and multi-agent systems. This second edition reflects the technological and research advancements, and presents the best examples of artificial life software models developed in the World and available for users.
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Flying insects are intelligent micromachines capable of exquisite maneuvers in unpredictable environments. Understanding these systems advances our knowledge of flight control, sensor suites, and unsteady aerodynamics, which is of crucial interest to engineers developing intelligent flying robots or micro air vehicles (MAVs). The insights we gain when synthesizing bioinspired systems can in turn benefit the fields of neurophysiology, ethology and zoology by providing real-life tests of the proposed models.
This book was written by biologists and engineers leading the research in this crossdisciplinary field. It examines all aspects of the mechanics, technology and intelligence of insects and insectoids. After introductory-level overviews of flight control in insects, dedicated chapters focus on the development of autonomous flying systems using biological principles to sense their surroundings and autonomously navigate. A significant part of the book is dedicated to the mechanics and control of flapping wings both in insects and artificial systems. Finally hybrid locomotion, energy harvesting and manufacturing of small flying robots are covered. A particular feature of the book is the depth on realization topics such as control engineering, electronics, mechanics, optics, robotics and manufacturing.
This book will be of interest to academic and industrial researchers engaged with theory and engineering in the domains of aerial robotics, artificial intelligence, and entomology.
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This book constitutes the refereed proceedings of the Second International Conference on Virtual Worlds, VW 2000, held in Paris, France, in July 2000. The 26 revised full papers presented together with two invited contributions were carefully reviewed and selected from numerous submissions. The book is divided into topical sections on virtual worlds communities and applications, virtual worlds technologies and tools, virtual humans and avatars, art and virtual worlds, artificial life and complex systems, and virtual reality and interfaces.
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An introduction to the coming revolution in art, technology, media, chemistry, science, and music discusses amino chemistry, manotechnology, high-tech paganism, teledildonics, and more. $75,000 ad/promo.
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The patterns on the shells of tropical sea snails are not only compellingly beautiful but also tell a tale of biological development. The decorative patterns are records of their own genesis, which follows laws like those of dune formation or the spread of a flu epidemic. Hans Meinhardt has analyzed the dynamical processes that form these patterns and retraced them faithfully in computer simulations. His book is exciting not only for the astonishing scientific knowledge it reveals but also for its fascinating pictures. An accompanying CD-ROM with the corresponding algorithms offers wide scopes to those who whish to try their hand at simulating and varying the patterns.
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In The Brain from 25,000 Feet, Mark A. Changizi defends a non-reductionist philosophy and applies it to a variety of problems in the brain sciences. Some of the key questions answered are as follows. Why do we see visual illusions, and why are illusions inevitable for any finite-speed vision machine? Why aren't brains universal learning machines, and what does the riddle of induction and its solution have to do with human learning and innateness? The author tackles such questions as why the brain is folded, and why animals have as many limbs as they do, explaining how these relate to principles of network optimality. He describes how most natural language words are vague and then goes on to explain the connection to the ultimate computational limits on machines. There is also a fascinating discussion of how animals accommodate greater behavioral complexity. This book is a must-read for researchers interested in taking a high-level, non-mechanistic approach to answering age-old fundamental questions in the brain sciences.
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A comprehensive guide to a powerful new analytical tool by two of its foremost innovators
The past decade has witnessed many exciting advances in the use of genetic algorithms (GAs) to solve optimization problems in everything from product design to scheduling and client/server networking. Aided by GAs, analysts and designers now routinely evolve solutions to complex combinatorial and multiobjective optimization problems with an ease and rapidity unthinkable withconventional methods. Despite the continued growth and refinement of this powerful analytical tool, there continues to be a lack of up-to-date guides to contemporary GA optimization principles and practices. Written by two of the world's leading experts in the field, this book fills that gap in the literature.
Taking an intuitive approach, Mitsuo Gen and Runwei Cheng employ numerous illustrations and real-world examples to help readers gain a thorough understanding of basic GA concepts-including encoding, adaptation, and genetic optimizations-and to show how GAs can be used to solve an array of constrained, combinatorial, multiobjective, and fuzzy optimization problems. Focusing on problems commonly encountered in industry-especially in manufacturing-Professors Gen and Cheng provide in-depth coverage of advanced GA techniques for:
* Reliability design
* Manufacturing cell design
* Scheduling
* Advanced transportation problems
* Network design and routing
Genetic Algorithms and Engineering Optimization is an indispensable working resource for industrial engineers and designers, as well as systems analysts, operations researchers, and management scientists working in manufacturing and related industries. It also makes an excellent primary or supplementary text for advanced courses in industrial engineering, management science, operations research, computer science, and artificial intelligence.
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"This book lays out a vision for a coherent framework for understanding complex systems'' (from the foreword by J. Doyne Farmer). By developing the genuine idea of Brownian agents, the author combines concepts from informatics, such as multiagent systems, with approaches of statistical many-particle physics. This way, an efficient method for computer simulations of complex systems is developed which is also accessible to analytical investigations and quantitative predictions. The book demonstrates that Brownian agent models can be successfully applied in many different contexts, ranging from physicochemical pattern formation, to active motion and swarming in biological systems, to self-assembling of networks, evolutionary optimization, urban growth, economic agglomeration and even social systems.
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