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• PREFACE; CONTENTS; Part One MATHEMATICAL THEORY; Connectionist Learning Through Gradient Following; INTRODUCTION; CONNECTIONIST SYSTEMS; LEARNING; Supervised Learning vs. Associative Reinforcement Learning; FORMAL ASSUMPTIONS AND NOTATION; BACK-PROPAGATION ALGORITHM FOR SUPERVISED LEARNING; Extended Back-Propagation; REINFORCE ALGORITHMS FOR ASSOCIATIVE REINFORCEMENT LEARNING; Extended REINFORCE Algorithms; DISCUSSION; SUMMARY; REFERENCES; Efficient Stochastic Gradient Learning Algorithm for Neural Network; 1 Introduction; 2 Learning as Stochastic Gradient Descents.

• 3 Convergence Theorems for First Order Schemes4 Convergence of the Second Order Schemes; 5 Discussion; References; INFORMATION STORAGE IN FULLY CONNECTED NETWORKS; 1 INTRODUCTION; 1.1 Neural Networks; 1.2 Organisation; 1.3 Notation; 2 THE MODEL OF McCULLOCH-PITTS; 2.1 State-Theoretic Description; 2.2 Associative Memory; 3 THE OUTER-PRODUCT ALGORITHM; 3.1 The Model; 3.2 Storage Capacity; 4 SPECTRAL ALGORITHMS; 4.1 Outer-Products Revisited; 4.2 Constructive Spectral Approaches; 4.3 Basins of Attraction; 4.4 Choice of Eigenvalues; 5 COMPUTER SIMULATIONS; 6 DISCUSSION; A PROPOSITIONS.

• B OUTER-PRODUCT THEOREMSC PROOFS OF SPECTRAL THEOREMS; References; NEURONIC EQUATIONS AND THEIR SOLUTIONS;
• 1. Introduction; 1
• .1. Reminiscing; 1
• .2. The 1961 Model; 1
• .3. Notation;
• 2. Linear Separable NE; 2
• .1. Neuronic Equations; 2
• .2. Polygonal Inequalities; 2
• .3. Computation of the n-expansion of arbitrary l.s. functions; 2
• .4. Continuous versus discontinuous behaviour: transitions;
• 3. General Boolean NE; 3
• .1. Linearization in tensor space; 3
• .2. Next-state matrix; 3
• .3. Normal modes, attractors; 3
• .4. Synthesis of nets: the inverse problem; 3
• .5. Separable versus Boolean nets.

• Connections with spin formalismReferences; The Dynamics of Searches Directed by Genetic Algorithms; The Hyperplane Transformation.; The Genetic Algorithm as a Hyperplane-Directed Search Procedure; (1) Description of the genetic algorithm; (2) Effects of the S's on the search generated by a genetic algorithm.; (3) An Example.; References.; PROBABILISTIC NEURAL NETWORKS;
• 1. INTRODUCTION;
• 2. MODELING THE NOISY NEURON; 2
• .1. Empirical Properties of Neuron and Synapse;
• 22. Model of Shaw and Vasudevan; 2
• .3. Model of Little; 2
• .4. Model of Taylor.

• 3. NONEQUILIBRIUM STATISTICAL MECHANICS OF LINEAR MODELS3.1. Statistical Law of Motion
• Markov Chain and Master Equation; 3.2. Entropy Production in the Neural; 3.3. Macroscopic Forces and Fluxes; 3.4. Conditions for Thermodynamic Equilibrium; 3.5. Implications for Memory Storage: How Dire?; 4. DYNAMICAL PROPERTIES OF NONLINEAR MODELS; 4.1. Views of Statistical Dynamics; 4.2. Multineuron Interactions, Revisited; 4.3. Cognitive Aspects of the Taylor Model; 4.4. Noisy RAMS and Noisy Nets; 5. THE END OF THE BEGINNING; ACKNOWLEDGMENTS; APPENDIX. TRANSITION PROBABILITIES IN 2-NEURON NETWORKS.

This review volume represents the first attempt to provide a comprehensive overview of this exciting and rapidly evolving development. The book comprises specially commissioned articles by leading researchers in the areas of neural networks and connectionist systems, classifier systems, adaptive network systems, genetic algorithm, cellular automata, artificial immune systems, evolutionary genetics, cognitive science, optical computing, combinatorial optimization, and cybernetics.
(source: Nielsen Book Data)

Building a person has been an elusive goal in artificial intelligence. This failure, John Pollock argues, is because the problems involved are essentially philosophical; what is needed for the construction of a person is a physical system that mimics human rationality. Pollock describes an exciting theory of rationality and its partial implementation in OSCAR, a computer system whose descendants will literally be persons.In developing the philosophical superstructure for this bold undertaking, Pollock defends the conception of man as an intelligent machine and argues that mental states are physical states and persons are physical objects as described in the fable of Oscar, the self conscious machine.Pollock brings a unique blend of philosophy and artificial intelligence to bear on the vexing problem of how to construct a physical system that thinks, is self conscious, has desires, fears, intentions, and a full range of mental states. He brings together an impressive array of technical work in philosophy to drive theory construction in AI. The result is described in his final chapter on "cognitive carpentry." John Pollock is Professor of Philosophy and Cognitive Science at the University of Arizona. A Bradford Book.
(source: Nielsen Book Data)

• An intelligent image-based computer-aided education system: the prototype BIRDS / A.A. David, O. Thiery & M. Crehange
• PLAYMAKER: a knowledge-based approach to characterizing hydrocarbon plays / G. Biswas [and others]
• An expert system for interpreting mesoscale features in oceanographic satellite images / N. Krishnakumar [and others]
• An expert system for tuning particle beam accelerators / D.L. Lager, H.R. Brand & W.J. Maurer
• Expert system approach to assessments of bleeding predispositions in tonsillectomy/adenoidectomy patients / N.J. Pizzi & J.M. Gerrard
• Expert system approach using graph representation and analysis for variable-stroke internal-combustion engine design / S.N.T. Shen, M.S. Chew & G.F. Issa
• A comparison of two new techniques for conceptual clustering / S.L. Crawford & S.K. Souders
• Querying an object-oriented database using free language / P. Trigano [and others]
• Adaptive planning for air combat maneuvering / I.C. Hayslip, J.P. Rosenking & J. Filbert
• AM/AG model: a hierarchical social system metaphor for distributed problem solving / D.G. Shin & J. Leone
• CAUSA
• A tool for model-based knowledge acquisition / W. Dilger & J. Moller
• PRIOPS: a real-time production system architecture for programming and learning in embedded systems / D.E. Parson & G.D. Blank.

This volume contains a well-balanced set of applications and theory papers in artificial intelligence advances. The applications papers each discuss a system that is (or is close to being) a fielded system that solves real problems using one or more AI techniques. They cover areas such as education, physics, energy, control, medicine and mechanical engineering.The theory papers, representing recent advances in various theoretical aspects of AI technology, concern themselves with "building block" issues, i.e. theories, algorithms, architectures, and software tools that can or will be used for modules within future systems. The topics covered are: clustering, natural language, adaptive algorithms, distributed processing, knowledge acquisition, and systems programming.
(source: Nielsen Book Data)

For centuries, people have been fascinated by the possibility of building an artificial system that behaves intelligently. Now there is a new entry in this arena - neural networks. Naturally Intelligent Systems offers a comprehensive introduction to these exciting systems. It provides a technically accurate, yet down-to-earth discussion of neural networks, clearly explaining the underlying concepts of key neural network designs, how they are trained, and why they work. Throughout, the authors present actual applications that illustrate neural networks' utility in the new world.
(source: Nielsen Book Data)
Naturally Intelligent Systems offers a comprehensive introduction to neural networks.
(source: Nielsen Book Data)
For centuries, people have been fascinated by the possibility of building an artificial system that behaves intelligently. From Mary Shelley's Frankenstein monster to the computer intelligence of HAL in 2001, scientists have been cast in the role of creator of such devices. Now there is a new entry into this arena, neural networks, and "Naturally Intelligent Systems explores these systems to see how they work and what they can do. Neural networks are not computers in any traditional sense, and they have little in common with earlier approaches to the problem of fabricating intelligent behavior. Instead, they are information processing systems that are physically modeled after the structure of the brain and that are "trained to perform a task rather than programmed like a computer. Neural networks, in fact, provide a tool with problemsolving capabilities - and limitations - strikingly similar to those of animals and people. In particular, they are successful in applications such as speech, vision, robotics, and pattern recognition. "Naturally Intelligent Systems offers a comprehensive introduction to these exciting systems. It provides a technically accurate, yet down-to-earth discussion of neural networks. No particular mathematical background is necessary; it is written for all interested readers. "Naturally Intelligent Systents clearly explains the underlying concepts of key neural network designs, how they are trained, and why they work. It compares their behavior to the natural intelligence found in animals - and people. Throughout, Caudill and Butler bring the field into focus by presenting actual applications that illustrate neural networks' utility in the real world. MaureenCaudill is President of Adaptics, a neural network consulting company in San Diego and author of the popular "Neural Network Primer" articles that appear regularly in "AI Expert. Charles Butler is a Senior Principal Scientist at Physical Sciences in Alexandria, Virginia. He is a specialist in neural network application development. A Bradford Book.
(source: Nielsen Book Data)

• 1. Neural networks: hopes, problems, and goals
• 2. The loading problem
• 3. Other studies of learning
• 4. The intractability of loading
• 5. Subcases
• 6. Shallow architectures
• 7. Memorization and generalization
• 8. Conclusion.

Using the tools of complexity theory, Stephen Judd develops a formal description of associative learning in connectionist networks. He rigorously exposes the computational difficulties in training neural networks and explores how certain design principles will or will not make the problems easier.Judd looks beyond the scope of any one particular learning rule, at a level above the details of neurons. There he finds new issues that arise when great numbers of neurons are employed and he offers fresh insights into design principles that could guide the construction of artificial and biological neural networks.The first part of the book describes the motivations and goals of the study and relates them to current scientific theory. It provides an overview of the major ideas, formulates the general learning problem with an eye to the computational complexity of the task, reviews current theory on learning, relates the book's model of learning to other models outside the connectionist paradigm, and sets out to examine scale-up issues in connectionist learning.Later chapters prove the intractability of the general case of memorizing in networks, elaborate on implications of this intractability and point out several corollaries applying to various special subcases. Judd refines the distinctive characteristics of the difficulties with families of shallow networks, addresses concerns about the ability of neural networks to generalize, and summarizes the results, implications, and possible extensions of the work. Neural Network Design and the Complexity of Learning is included in the Network Modeling and Connectionism series edited by Jeffrey Elman.
(source: Nielsen Book Data)

Both specialists and laymen will enjoy reading this book. Using a lively, non-technical style and images from everyday life, the authors present the basic principles behind computing and computers. The focus is on those aspects of computation that concern networks of numerous small computational units, whether biological neural networks or artificial electronic devices.
(source: Nielsen Book Data)

• Ch 1. Embedding learning in a general frame-based architecture / T. Tanaka and T.M. Mitchell
• ch. 2. Connectionist learning with Chebychev networks and analyses of its internal representation / A. Narnatame
• ch. 3. Layered inductive learning algorithms and their computational aspects / H. Madala
• ch. 4. An approach to combining explanation-based and neural learning algorithms / J.W. Shavlik and G.G. Towell
• ch. 5. The application of symbolic inductive learning to the acquisition and recognition of noisy texture concepts / P.W. Pachowicz
• ch. 6. Automating technology adaptation in design synthesis / J.R. Kipps and D.D. Gajski
• ch. 7. Connectionist production systems in local and hierarchical representation / A. Sohn and J.-L. Gaudiot
• ch. 8. A parallel architecture for AI nonlinear planning / S. Lee and K. Chung
• ch. 9. Heuristic tree search using nonparametric statistical inference methods / W. Zhang and N.S.V. Rao
• ch. 10. An A* approach to robust plan recognition for intelligent interfaces / R.J. Calistri-Yeh
• ch. 11. Differential A*: an adaptive search method illustrated with robot path planning for moving obstacles & goals, and an uncertain environment / K.I. Trovato
• ch. 12. Path planning under uncertainty / F. Yegenoglu and H.E. Stephanou
• ch. 13. Knowledge-based acquisition in real-time path planning in unknown space / N.G. Bourbakis
• ch. 14. Path planning for two cooperating robot manipulators / Q. Xue and P.C.-Y. Sheu.

Learning and planning are two important topics of artificial intelligence. Learning deals with the algorithmic processes that make a computing machine able to "learn" and improve its performance during the process of complex tasks. Planning on the other hand, deals with decision and construction processes that make a machine capable of constructing an intelligent plan for the solution of a particular complex problem.This book combines both learning and planning methodologies and their applications in different domains. It is divided into two parts. The first part contains seven chapters on the ongoing research work in symbolic and connectionist learning. The second part includes seven chapters which provide the current research efforts in planning methodologies and their application to robotics.
(source: Nielsen Book Data)

• Limited rationality
• execution architectures for decision procedures
• metareasoning architecture
• rational metareasoning
• application to game playing
• application to problem solving search
• learning the value of computation
• toward limited rational agents.
• (source: Nielsen Book Data)

Like Mooki, the hero of Spike Lee's film "Do the Right Thing, " artificially intelligent systems have a hard time knowing what to do in all circumstances. Classical theories of perfect rationality prescribe the "right thing" for any occasion, but no finite agent can compute their prescriptions fast enough. In "Do the Right Thing, " the authors argue that a new theoretical foundation for artificial intelligence can be constructed in which rationality is a property of "programs" within a finite architecture, and their behavior over time in the task environment, rather than a property of individual decisions."Do the Right Thing" suggests that the rich structure that seems to be exhibited by humans, and ought to be exhibited by AI systems, is a necessary result of the pressure for optimal behavior operating within a system of strictly limited resources. It provides an outline for the design of new intelligent systems and describes theoretical and practical tools for bringing about intelligent behavior in finite machines. The tools are applied to game planning and real-time problem solving, with surprising results.Contents Limited Rationality. Execution Architectures for Decision Procedures. Metareasoning Architecture. Rational Metareasoning. Application to Game Playing. Application to Problem Solving Search. Learning the Value of Computation. Toward Limited Rational Agents.
(source: Nielsen Book Data)
Like Mooki, the hero of Spike Lee's film "Do the Right Thing" artificially, intelligent systems have a hard time knowing what to do in all circumstances. Classical theories of perfect rationality prescribe the "right thing" for any occasion, but no finite agent can compute their prescriptions fast enough. In "Do the Right Thing", the authors argue that a new theoretical foundation for artificial intelligence can be constructed in which rationality is a property of "programs" within a finite architecture, and their behaviour over time in the task environment, rather than a property of individual decisions. "Do The Right Thing" suggests that the rich structure that seems to be exhibited by humans, and ought to be exhibited by AI systems, is a necessary result of the pressure for optimal behaviour operating within a system of strictly limited resources. It provides an outline for the design of new intelligent systems and describes theoretical and practical tools for bringing about intelligent behaviour in finite machines. The tools are applied to game planning and real-time problem solving, with surprising results.
(source: Nielsen Book Data)

• AUTHORS' NOTES AND ACKNOWLEDGEMENTS; INTRODUCTION; 1.1. Objectives of this Book; 1.2. The Seeing Eye and the Knowing Eye
• 1 IMAGE AND COMPUTER; 1.1. A Short History; 1.2. The Computer's Eye; 1.3. A Beetle and an Ant-Hill; 1.4. Features and Models; 2 HOW HUMANS SEE THE WORLD; 2.1. The Eye and the Brain; 2.2. The Level of Preattention; 2.3. Right and Left Vision; 2.4. Images and Words; 3 CONVERSATIONS WITH A COMPUTER; 3.1. From a Point to a Region; 3.2. From a Region to an Object; 3.3. From an Object to a Situation; 4 AN APOLOGIA FOR VISION; 4.1. The Evolution of Vision.

• 4
• .2. Vision and Thinking4
• .3. Recollection of the Future; 4
• .4. Cognition through Vision; 5 CREATING A NEW WORLD; 5
• .1. From Elements to the System; 5
• .2. Back to Nature; 5
• .3. Who Do We Think They Are?; CONCLUSIONS; PLATES; REFERENCES; ILLUSTRATIONS; INDEX.

This book considers computer vision to be an integral part of the artificial intelligence system. The core of the book is an analysis of possible approaches to the creation of artificial vision systems, which simulate human visual perception. Much attention is paid to the latest achievements in visual psychology and physiology, the description of the functional and structural organization of the human perception mechanism, the peculiarities of artistic perception and the expression of reality. Computer vision models based on these data are investigated. They include the processes of external data analysis, internal environmental model synthesis, and the generating of behavioristic responses based on external and internal models comparison. Computer vision system evolution resulting from environmental effects is also considered. A unique feature of this book is the authors' use of black and white, and colour prints of traditional and contemporary Russian art to illustrate their principal theses. In doing so, they introduce the reader to a particularly Russian view of the world.
(source: Nielsen Book Data)

• Introduction, C.H. Chen
• combined neural-net/knowledge-based adaptive systems for large scale dynamic control, A.D.C. Holden and S.C. Suddarth
• a connectionist incremental expert system combining production systems and associative memory, H.F. Yin and P. Liang
• optimal hidden units for two-layer nonlinear feedforward networks, T.D. Sanger
• an incremental fine adjustment algorithm for the design of optimal interpolating networks, S.K. Sin and R.J.P. deFigueiredo
• on the asymptotic properties of recurrent neural networks for optimization, J. Wang
• a real-time image segmentation system using a connectionist classifier architecture, W.E. Blanz and S.L. Gish
• segmentation of ultrasonic images with neural network technology's on automatic active sonar classifier development, T.B. Haley
• on the relationships between statistical pattern recognition and artificial neural networks, C.H. Chen.
• (source: Nielsen Book Data)

The revitalization of neural network research in the past few years has already had a great impact on research and development in pattern recognition and artificial intelligence. Although neural network functions are not limited to pattern recognition, there is no doubt that a renewed progress in pattern recognition and its applications now critically depends on neural networks. This volume specially brings together outstanding original research papers in the area and aims to help the continued progress in pattern recognition and its applications.
(source: Nielsen Book Data)

• An introduction to artificial intelligence, N.G. Bourbakis
• fundamental methods for horn logic and AI applications, E. Kounalis and P. Marquis
• applications of genetic algorithms to permutation problems, F. Petry and B. Buckles
• extracting procedural knowledge from software systems using inductive learning in the PM system, R. Reynolds and E. Zannoni
• resource oriented parallel planning, S. Lee and K. Chung
• advanced parsing technology for knowledge based shells, J. Kipps
• analysis and synthesis of intelligent systems, W. Arden
• document analysis and recognition, S.N. Srihari et al
• signal understanding - an AI approach to modulation and classification, J.E. Whelchel et al
• and others.
• (source: Nielsen Book Data)

This volume is the first in a series which deals with the challenge of AI issues, gives updates of AI methods and applications, and promotes high quality new ideas, techniques and methodologies in AI. This volume contains articles by 38 specialists in various AI subfields covering theoretical and application issues.
(source: Nielsen Book Data)

• Ten years of research in artificial intelligence (1957-1967)
• Cognitive simulation (1957-1962)
• Semantic information processing (1962-1967)
• Assumptions underlying persistent optimism
• Biological assumption
• Psychological assumption
• Epistemological assumption
• Ontological assumption
• Alternatives to the traditional assumptions
• The role of the body in intelligent behavior
• The situation: orderly behavior without recourse to rules
• The situation as a function of human needs
• Conclusion: the scope and limits of artificial reason
• The limits of artificial intelligence
• The future of artificial intelligence.

When it was first published in 1972, Hubert Dreyfus's manifesto on the inherent inability of disembodied machines to mimic higher mental functions caused an uproar in the artificial intelligence community. The world has changed since then. Today it is clear that "good old-fashioned AI, " based on the idea of using symbolic representations to produce general intelligence, is in decline (although several believers still pursue its pot of gold), and the focus of the AI community has shifted to more complex models of the mind. It has also become more common for AI researchers to seek out and study philosophy. For this edition of his now classic book, Dreyfus has added a lengthy new introduction outlining these changes and assessing the paradigms of connectionism and neural networks that have transformed the field. At a time when researchers were proposing grand plans for general problem solvers and automatic translation machines, Dreyfus predicted that they would fail because their conception of mental functioning was naive, and he suggested that they would do well to acquaint themselves with modern philosophical approaches to human being. "What Computers Still Can't Do" was widely attacked but quietly studied. Dreyfus's arguments are still provocative and focus our attention once again on what it is that makes human beings unique.
(source: Nielsen Book Data)

• Part 1 Melting the inner code: computational models, syntax, and the folk solids
• connectionism, code, and context
• what networks know
• what networks don't know
• concept, category and prototype. Part 2 From code to process: the presence of a symbol
• the role of representational trajectories
• the cascade of significant virtual machines
• associative learning in a hostile world
• the fate of the folk
• associative engines - the next generation.
• (source: Nielsen Book Data)

Connectionist approaches, Andy Clark argues, are driving cognitive science toward a radical reconception of its explanatory endeavour. At the heart of this reconception lies a shift toward a new and more deeply developmental vision of the mind - a vision that has important implications for the philosophical and psychological understanding of the nature of concepts, of mental causation, and of representational change. Combining philosophical argument, empirical results, and interdisciplinary speculations, Clark charts a fundamental shift from a static, inner-code-oriented conception of the subject matter of cognitive science to a more dynamic, developmentally rich, process-oriented view. Clark argues that this shift makes itself felt in two main ways. First, structured representations are seen as the products of temporally extended cognitive activity and not as the representational bedrock (an innate symbol system or language of thought) upon which all learning is based. Second, the relation between thoughts (as described by folk psychology) and inner computational states is loosened as a result of the fragmented and distributed nature of the connectionist representation of concepts. Other issues Clark raises include the nature of innate knowledge, the conceptual commitments of folk psychology, and the use and abuse of higher-level analyses of connectionist networks.
(source: Nielsen Book Data)

• 1. Introduction and important definitions
• 2. Representation issues
• 3. Perceptron learning and the pocket algorithm
• 4. Winner-take-all groups or linear machines
• 5. Autoassociators and one-shot learning
• 6. Mean squared error (MSE) algorithms
• 7. Unsupervised learning
• 8. The distributed method and radial basis functions
• 9. Computational learning theory and the BRD algorithm
• 10. Constructive algorithms
• 11. Backpropagation
• 12. Backpropagation : variations and applications
• 13. Simulated annealing and boltzmann machines
• 14. Expert systems and neural networks
• 15. Details of the MACIE system
• 16. Noise, redundancy, fault detection, and bayesian decision theory
• 17. Extracting rules from networks.

Neural Network Learning and Expert Systems is the first book to present a unified and in-depth development of neural network learning algorithms and neural network expert systems. Especially suitable for students and researchers in computer science, engineering, and psychology, this text and reference provides a systematic development of neural network learning algorithms from a computational perspective, coupled with an extensive exploration of neural network expert systems which shows how the power of neural network learning can be harnessed to generate expert systems automatically. Features include a comprehensive treatment of the standard learning algorithms (with many proofs), along with much original research on algorithms and expert systems. Additional chapters explore constructive algorithms, introduce computational learning theory, and focus on expert system applications to noisy and redundant problems. For students there is a large collection of exercises, as well as a series of programming projects that lead to an extensive neural network software package. All of the neural network models examined can be implemented using standard programming languages on a microcomputer.
(source: Nielsen Book Data)

• A perspective on the work in this book / Kenneth E. Kinnear, Jr.
• Introduction to genetic programming / John R. Koza
• The evolution of evolvability in genetic programming / Lee Altenberg
• Genetic programming and emergent intelligence / Peter J. Angeline
• Scalable learning in genetic programming using automatic function definition / John R. Koza
• Alternatives in automatic function definition : a comparison of performance / Kenneth E. Kinnear, Jr.
• The donut problem : scalability, generalization and breeding policies in genetic programming / Walter Alden Tackett, Aviram Carmi
• Effects of locality in individual and population evolution / Patrik D'haeseleer, Jason Bluming
• The evolution of mental models / Astro Teller
• Evolution of obstacle avoidance behavior : using noise to promote robust solutions / Craig W. Reynolds
• Pygmies and civil servants / Conor Ryan
• Genetic programming using a minimum decsription length principle / Hitoshi Iba, Hugo de Garis, Taisuke Sato
• Genetic programming in C++: implementation issues / Mike J. Keith, Martin C. Martin. A compiling genetic programming system that directly manipulates the machine code / Peter Nordin
• Automatic generation of programs for crawling and walking / Graham Spencer
• Genetic programming for the acquisition of double auction market strategies / Martin Andrews, Richard Prager
• Two scientific applications of genetic programming : stack filters and non-linear equation fitting to chaotic data / Howard Oakley
• The automatic generation of plans for a mobile robot via genetic programming with automatically defined functions / Simon G. Handley
• Competitively evolving decision trees against fixed training cases for natural language processing / Eric V. Siegel
• Cracking and co-evolving randomizers / Jan Jannink
• Optimizing confidence of text classification by evolution of symbolic expressions / Brij Masand
• Evolvable 3D modeling for model-based object recognition systems / Thang Nguyen, Thomas Huang
• Automatically defined features : the simultaneous evolution of 2-dimensional feature detectors and an algorithm for using them / David Andre
• Genetic micro programming of neural networks / Frédéric Gruau.

Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in manu of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public-domain code is available, and on how to become part of the active genetic programming community via electronic mail.
(source: Nielsen Book Data)
There is increasing interest in genetic programming by both researchers and professional software developers. These twenty-two invited contributions show how a wide variety of problems across disciplines can be solved using this new paradigm. Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in many of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public domain code is available, and on how to become part of the active genetic programming community via electronic mail. A major focus of the book is on improving the power of genetic programming. Experimental results are presented in a variety of areas, including adding memory to genetic programming, using locality and "demes" to maintain evolutionary diversity, avoiding the traps of local optima by using coevolution, using noise to increase generality, and limiting the size of evolved solutions to improve generality. Significant theoretical results in the understanding of the processes underlying genetic programming are presented, as are several results in the area of automatic function definition. Performance increases are demonstrated by directly evolving machine code, and implementation and design issues for genetic programming in C++ are discussed.
(source: Nielsen Book Data)

• Computers and computation
• the discrete neuron
• the Boolean neuron
• alternating circuits
• small, shallow alternating circuits
• threshold circuits
• cyclic networks
• probabilistic neural networks.
• (source: Nielsen Book Data)

Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how well neural networks scale - that is, how fast the computation time and number of neurons grow as the problem size increases. It surveys recent research in circuit complexity (a robust branch of theoretical computer science) and applies this work to a theoretical understanding of the problem of scalability.Most research in neural networks focuses on learning, yet it is important to understand the physical limitations of the network before the resources needed to solve a certain problem can be calculated. One of the aims of this book is to compare the complexity of neural networks and the complexity of conventional computers, looking at the computational ability and resources (neurons and time) that are a necessary part of the foundations of neural network learning.Circuit Complexity and Neural Networks contains a significant amount of background material on conventional complexity theory that will enable neural network scientists to learn about how complexity theory applies to their discipline, and allow complexity theorists to see how their discipline applies to neural networks.
(source: Nielsen Book Data)
Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how well neural networks scale - that is, how fast the computation time and number of neurons grow as the problem size increases. It surveys recent research in circuit complexity (a robust branch of theoretical computer science) and applies this work to a theoretical understanding of the problem of scalability.
(source: Nielsen Book Data)

• The probably approximately correct learning model
• Occam's razor
• the Vapnik-Chervonenkis dimension
• weak and strong learning
• learning in the presence of noise
• inherent unpredictability
• reducibility in PAC learning
• learning finite automata by experimentation
• appendix - some tools for probabilistic analysis.
• (source: Nielsen Book Data)

Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.
(source: Nielsen Book Data)

• Introduction: the Turing test in light of the frame problem
• Algorithmic machines and computer learning
• Computer simulation and user illusions
• The Dreyfus critique and the frame problem
• The Turing test, Dennett's defense of the test, and mindlike programs
• The relation of the frame problem to the Turing test
• Two major critiques of the test: Searle and Gunderson
• The frame problem, philosophy, and AI's understanding of intelligence.

Both the Turing test and the frame problem have been significant items of discussion since the 1970s in the philosophy of artificial intelligence (AI) and the philisophy of mind. However, there has been little effort during that time to distill how the frame problem bears on the Turing test. If it proves not to be solvable, then not only will the test not be passed, but it will call into question the assumption of classical AI that intelligence is the manipluation of formal constituens under the control of a program. This text explains why there has been less progress in artificial intelligence research than AI proponents would have believed in the mid-1970s. As a first pass, the difficulty of the frame problem would account for some of the lack of progress. An alternative interpretation is that the research paradigm itself is destined to be less productive than might have been hoped. In general termns, the view advanced here is that the future of AI depends on whether the frame problem eventually falls to computational techniques. If it turns out that the frame problem is computationally irreducible, of there is no way to solve it computationally by means of a program operating on formally defined constituents, then an increasing number of experts in the field will reach the conclusion that AI embodies a fundamental misunderstanding of intelligence.
(source: Nielsen Book Data)

• Mechanisms of mind
• the nature of mind and the mind-body problem
• animal minds
• symbolic AI
• the first AI debate
• connectionism
• the second AI debate
• evolution, natural and artificial
• artificial life
• multiplicity of mind
• what do I do now?
• what's out there?
• remembering and creating
• representation and the third AI debate
• into the future
• an emerging new paradigm of mind?.
• (source: Nielsen Book Data)

Recent decades have produced a blossoming of research in artificial systems that exhibit important properties of mind. But what exactly is this dramatic new work and how does it change the way we think about the mind, or even about who or what has mind?Stan Franklin is the perfect tour guide through the contemporary interdisciplinary matrix of artificial intelligence, cognitive science, cognitive neuroscience, artificial neural networks, artificial life, and robotics that is producing a new paradigm of mind. Leisurely and informal, but always informed, his tour touches on all of the major facets of mechanisms of mind.Along the way, Franklin makes the case for a perspective that rejects a rigid distinction between mind and non-mind in favor of a continuum from less to more mind, and for the role of mind as a control structure with the essential task of choosing the next action. Selected stops include the best of the work in these different fields, with the key concepts and results explained in just enough detail to allow readers to decide for themselves why the work is significant.Major attractions include animal minds, Allan Newell's SOAR, the three Artificial Intelligence debates, John Holland's genetic algorithms, Wilson's Animat, Brooks' subsumption architecture, Jackson's pandemonium theory, Ornstein's multimind, Marvin Minsky's society of mind, Pattie Maes's behavior networks, Gerald Edelman's neural Darwinism, Drescher's schema mechanisms, Pentti Kanerva's sparse distributed memory, Douglas Hofstadter and Melanie Mitchell's Copycat, and Agre and Chapman's deictic representations. A Bradford Book.
(source: Nielsen Book Data)
Recent decades have produced a blossoming of research in artificial systems that exhibit important properties of mind. But what exactly is this dramatic new work and how does it change the way we think about the mind, or even about who or what has mind?
(source: Nielsen Book Data)

• Fuzzy logic and genetic algorithms
• learning
• fuzzy and hybrid systems
• decision and aggregation techniques
• fuzzy logic in databases
• foundations of fuzzy logic
• applications of fuzzy sets.
• (source: Nielsen Book Data)

Soft computing is a new, emerging discipline rooted in a group of technologies that aim to exploit the tolerance for imprecision and uncertainty in achieving solutions to complex problems. The principal components of soft computing are fuzzy logic, neurocomputing, genetic algorithms and probabilistic reasoning.This volume is a collection of up-to-date articles giving a snapshot of the current state of the field. It covers the whole expanse, from theoretical foundations to applications. The contributors are among the world leaders in the field.
(source: Nielsen Book Data)

• 1. Learning, goals, and learning goals / by Ashwin Ram and David B. Leake
• 2. Planning to learn / by Lawrence Hunter
• 3. Quantitative results concerning the utility of explanation-based learning / by Steven Minton
• 4. The use of explicit goals for knowledge to guide inference and learning / by Ashwin Ram and Lawrence Hunter
• 5. Deriving categories to achieve goals / by Lawrence W. Barsalou
• 6. Harpoons and long sticks : the interaction of theory and similarity in rule induction / by Edward J. Wisniewski and Douglas L. Medin
• 7. Introspective reasoning using meta-explanations for multistrategy learning / by Ashwin Ram and Michael T. Cox
• 8. Goal-directed learning : a decision-theoretic model for deciding what to learn next / by Marie desJardins
• 9. Goal-based explanation evaluation / by David B. Leake
• 10. Planning to perceive / by Louise Pryor and Gregg Collins
• 11. Planning and learning in PRODIGY : overview of an integrated architecture / by Jaime Carboneil [and others]
• 12. A learning model for the selection of problem-solving strategies in continuous physical systems / by Xiaodong Xia and Dif-Yan Yeung
• 13. Explicitly biased generalization / by Diana Gordon and Donald Perlis
• 14. Three levels of goal orientation in learning / by Evelyn Ng and Carl Bereiter
• 15. Characterizing the application of computer simulations in education : instructional criteria / by Jos J.A. van Berkum [and others]
• 16. Goal-driven learning : fundamental issues and symposium report / by David B. Leake and Ashwin Ram
• 17. Storage side effects : studying processing to understand learning / by Lawrence W. Barsalou
• 18. Goal-driven learning in multistrategy reasoning and learning systems / by Ashwin Ram, Michael T. Cox and S. Narayanan
• 19. Inference to the best plan : a coherence theory of decision / by Paul Thagard and Elija Millgram
• 20. Toward goal-driven integration of explanation and action / by David B. Leake
• 21. Learning as goal-driven inference / by Ryszard Michalski and Ashwin Ram.

In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations. The book opens with an an overview of goal-driven learning research and computational and cognitive models of the goal-driven learning process. This introduction is followed by a collection of fourteen recent research articles addressing fundamental issues of the field, including psychological and functional arguments for modeling learning as a deliberative, planful process; experimental evaluation of the benefits of utility-based analysis to guide decisions about what to learn; case studies of computational models in which learning is driven by reasoning about learning goals; psychological evidence for human goal-driven learning; and the ramifications of goal-driven learning in educational contexts. The second part of the book presents six position papers reflecting ongoing research and current issues in goal-driven learning. Issues discussed include methods for pursuing psychological studies of goal-driven learning, frameworks for the design of active and multistrategy learning systems, and methods for selecting and balancing the goals that drive learning. A Bradford Book.
(source: Nielsen Book Data)
In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations.
(source: Nielsen Book Data)

Within artificial intelligence, the need to create sophisticated, intelligent behaviour based on common-sense reasoning has long been recognized. Research has demonstrated that formalism for dealing with common sense reasoning require nonmonotonic capabilities where, typically, inferences based on incomplete knowledge need to be revised in light of later information which fills in some of the gaps.
(source: Nielsen Book Data)

• The Road to Intelligent Machines
• The Power and Limitations of Perceptrons
• Neural Logic Networks
• Probabilistic Neural Logic Networks
• Fuzzy Neural Logic Networks
• Temporal Neural Logic Networks
• Neural Logic Programming
• Connectionist Expert Systems
• Fuzzy Knowledge Processing
• The Art of Guessing.
• (source: Nielsen Book Data)

This book is the first of a series of technical reports of a key research project of the Real-World Computing Program supported by the MITI of Japan.The main goal of the project is to model human intelligence by a special class of mathematical systems called neural logic networks.The book consists of three parts. Part 1 describes the general theory of neural logic networks and their potential applications. Part 2 discusses a new logic called Neural Logic which attempts to emulate more closely the logical thinking process of human. Part 3 studies the special features of neural logic networks which resemble the human intuition process.This book should appeal to researchers in artificial intelligence, neural computings and logic, as well as graduate and advance undergraduate students in computer science.
(source: Nielsen Book Data)

• From recognition to analogy-making - the central role of slippage
• the Tabletop microdomain
• the architecture of Tabletop
• Tabletop's performance up close
• Tabletop's personality profile
• comparisons with other work
• summary and conclusions.
• (source: Nielsen Book Data)

Foreword by Daniel Dennett While it is fashionable today to dismiss the "bad old days" of artificial intelligence and rave about emergent self-organizing systems, Robert French has created a model of human analogy-making that attempts to bridge the gap between classical top-down AI and more recent bottom-up approaches.
(source: Nielsen Book Data)
The research described in this book is based on the premise that human analogy-making is an extension of our constant background process of perceiving-in other words, that analogy-making and the perception of sameness are two sides of the same coin. Foreword by Daniel Dennett While it is fashionable today to dismiss the "bad old days" of artificial intelligence and rave about emergent self-organizing systems, Robert French has created a model of human analogy-making that attempts to bridge the gap between classical top-down AI and more recent bottom-up approaches. The research described in this book is based on the premise that human analogy-making is an extension of our constant background process of perceiving-in other words, that analogy-making and the perception of sameness are two sides of the same coin. At the heart of the author's theory and computer model of analogy-making is the idea that the building-up and the manipulation of representations are inseparable aspects of mental functioning, in contrast to traditional AI models of high-level cognitive processes, which have almost always depended on a clean separation. A computer program called Tabletop forms analogies in a microdomain consisting of everyday objects on a table set for a meal. The theory and the program rely on the idea that myriad stochastic choices made on the microlevel can add up to statistical robustness on a macrolevel. To illustrate this, French includes the results of thousands of runs of his program on several dozen interrelated analogy problems in the Tabletop microworld. French's work is exciting not only because it reveals analogy-making to be an extension of our complex and subtle ability to perceive sameness but also because it offers a computational model of mechanisms underlying these processes. This model makes significant strides in putting into practice microlevel stochastic processing, distributed processing, simulated parallelism, and the integration of representation-building and representation-processing. A Bradford Book.
(source: Nielsen Book Data)

• Neural network performance metrics
• noise in neural implementations
• simulation requirements and environment
• fault tolerance
• generalisation ability
• learning trajectory and speed.
• (source: Nielsen Book Data)

Hardware inaccuracy and imprecision are important considerations when implementing neural algorithms. This book presents a study of synaptic weight noise as a typical fault model for analogue VLSI realisations of MLP neural networks and examines the implications for learning and network performance. The aim of the book is to present a study of how including an imprecision model into a learning scheme as a"fault tolerance hint" can aid understanding of accuracy and precision requirements for a particular implementation. In addition the study shows how such a scheme can give rise to significant performance enhancement.
(source: Nielsen Book Data)

Machine learning is a relatively new branch of artificial intelligence. The field has undergone a significant period of growth in the 1990s, with many new areas of research and development being explored.
(source: Nielsen Book Data)

• PART I: A COMPARISON OF EVOLUTIONARY ALGORITHMS
• PART II: EXTENDING GENETIC ALGORITHMS.
• (source: Nielsen Book Data)

PART I: Comparison of Evolutionary Algorithms. 1. Organic Evolution and Problem Solving. 2. Specific Evolutionary Algorithms. 3. Artificial Landscapes. 4. An Empirical Comparison. PART II: Extending Genetic Algorithms. 5. Selection. 6. Mutation. 7. An Experiment in Meta-Evolution.
(source: Nielsen Book Data)
This book compares the three most prominent representatives of evolutionary algorithms - genetic algorithms, evolution strategies, and evolutionary programming - computational methods at the border between computer science and evolutionary biology. The algorithms are explained within a common formal framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms and uses a meta-evolutionary approach to confirm some of the theoretical results.
(source: Nielsen Book Data)

• 1. The faculty of knowledge engineering and problem solving
• 2. Knowledge engineering and symbolic artificial intelligence
• 3. From fuzzy sets to fuzzy systems
• 4. Neural networks : theoretical and computational models
• 5. Neural networks for knowledge engineering and problem solving
• 6. Hybrid symbolic, fuzzy, and connectionist systems : toward comprehensive artificial intelligence
• 7. Neural networks, fuzzy systems, and nonlinear dynamical systems. chaos ; toward new connectionist and fuzzy logic models.

"Covering the latest issues and achievements, this well documented, precisely presented text is timely and suitable for graduate and upper undergraduate students in knowledge engineering, intelligent systems, AI, neural networks, fuzzy systems, and related areas. The author's goal is to explain the principles of neural networks and fuzzy systems and to demonstrate how they can be applied to building knowledge-based systems for problem solving. Especially useful are the comparisons between different techniques (AI rule-based methods, fuzzy methods, connectionist methods, hybrid systems) used to solve the same or similar problems.".
(source: Nielsen Book Data)
Neural networks and fuzzy systems are different aprpoaches to introducing human-like reasoning into expert systems. This text combines the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems. Kasabov describes rule-based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particular feature of the text is that it is filled with applications in engineering, business and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples. Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, "Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering" has chapters structured for various levels of teaching and includes work by the author along with the classic material. Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.
(source: Nielsen Book Data)

• Hardware and software aspects
• transputer topologies for parallel implementation
• comparison between serial and parallel implementation
• analysis and implementation for equal distribution of the training set in a homogeneous transputer array
• analysis and implementation for unequal distribution of the training set in a homogeneous transputer array
• analysis and implementation for unequal distribution of the training set in a heterogeneous transputer array
• conclusion.
• (source: Nielsen Book Data)

This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers. The emphasis is on backpropagation learning and training set parallelism. Using systematic analysis, a theoretical model has been developed for the parallel implementation. The model is used to find the optimal mapping to minimize the training time for large backpropagation neural networks. The model has been validated experimentally on several well known benchmark problems. Use of genetic algorithms for optimizing the performance of the parallel implementations is described. Guidelines for efficient parallel implementations are highlighted.
(source: Nielsen Book Data)

• Fuzzy compensation principles
• normal fuzzy reasoning methodology
• compensatory genetic fuzzy neural networks
• fuzzy knowledge rediscovery in fuzzy rule bases
• fuzzy cat-pole balancing control systems
• fuzzy knowledge compression and expansion
• highly nonlinear system modelling and prediction
• fuzzy moves in fuzzy games
• genetic neuro-fuzzy pattern recognition
• constructive approach to modelling fuzzy systems.
• (source: Nielsen Book Data)

This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games and pattern recognition. This effective soft computing system is able to perform both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also proposes various novel soft computing techniques.
(source: Nielsen Book Data)

• Helicopter flight control with fuzzy logic and genetic algorithms, C. Philips et al
• skill acquisition and skill-based motion planning for hierarchical intelligent control of a redundant manipulator, T. Shibata
• a creative design of fuzzy logic controller using a genetic algorithm, T. Hashiyama et al
• automatic fuzzy tuning and its applications, H. Ishigami et al
• an evolutionary algorithm for fuzzy controller synthesis and optimization based on SGS-Thomson's W.A.R.P. fuzzy processor, R. Poluzzi et al
• on-line self-structuring fuzzy inference systems for function approximation, H. Bersini
• fuzzy classification based on adaptive networks and genetic algorithms, C.-T. Sun and J.-S. Jang
• intelligent systems for fraud detection, J. Kingdon
• genetic algorithms for query optimization in information retrieval - relevance feedback, D.H. Kraft et al
• fuzzy fitness assignment in an interactive genetic algorithm for a cartoon face search, K. Nishio et al
• an evolutionary approach to simulate cognitive feedback learning in medical domain, H.S. Lopes et al
• a classified review on the combination fuzzy logic-genetic algorithms bibliography - 1989-1995, O. Cordon et al.
• (source: Nielsen Book Data)

Ever since fuzzy logic was introduced by Lotfi Zadeh in the mid-sixties and genetic algorithms by John Holland in the early seventies, these two fields widely been subjects of academic research the world over. During the last few years, they have been experiencing extremely rapid growth in the industrial world, where they have been shown to be very effective in solving real-world problems. These two substantial fields, together with neurocomputing techniques, are recognized as major parts of soft computing: a set of computing technologies already riding the waves of the next century to produce the human-centered intelligent systems of tomorrow; the collection of papers presented in this book shows the way. The book also contains an extensive bibliography on fuzzy logic and genetic algorithms.
(source: Nielsen Book Data)

• A new way to acquire knowledge, H.-Y. Wang
• an SPN knowledge representation scheme, J. Gattiker and N. Bourbakis
• on the deep structures of word problems and their construction, F. Gomez
• resolving conflicts in inheritance reasoning with statistical approach, C. Lee
• integrating high and low level computer vision for scene understanding, R. Malik and S. So
• the evolution of commercial AI tools - the first decade, F. Hayes-Roth
• reengineering - the AT generation - billions on the table, J.S. Minor, Jr.
• an intelligent tool for discovering data dependencies in relational DBS, P. Gavaskar and F. Golshani
• a case-based reasoning (CBR) tool to assist traffic flow, B. Das and S. Bayles
• a study of financial expert system based on flops, T. Kaneko and K. Takenaka
• an associative data parallel compilation model for tight integration of high performance knowledge retrieval and computation, A. Bansal
• software automation - from silly to intelligent, X. Jiafu et al
• software engineering using artificial intelligence - the knowledge based software assistant, D. White
• knowledge based derivation of programmes from specs, T. Weight et al
• automatic functional model generation for parallel fault design error simulations, S.E. Chang and S. Szygenda
• visual reverse engineering using SPN for automated diagnosis and functional simulation of digital circuits, J. Gattiker and S. Mertoguno
• the impact of AI in VLSI design automation, M. Mortazavi and N. Bourbakis
• the automated acquisition of subcategorization of verbs, nouns and adjectives from sample sentences, F. Gomez
• general method for planning and rendezvous problems, K. Trovato
• learning to improve path planning performance, P.C. Chen
• incremental adaptation as a method to improve reactive behaviour, A.J. Hendriks and D.M. Lyons
• an SPN-neural planning methodology for coordination of multiple robotic arms with constrained placement, N. Bourbakis and A. Tascillo.
• (source: Nielsen Book Data)

Covering artificial intelligence and automation, these contributions discuss: the evolution of AI tools; an SPN knowledge representation scheme; software engineering using AI; the impact of AI in VLSI design automation; incremental adaptation as a method to improve reactive behaviour; and more.
(source: Nielsen Book Data)

• Approximate reasoning about comlpex objects in distributed systems - rough mereological formulation
• FOOD - fuzzy object-oriented design
• approximating block access in database systems
• the computer zoo in a box
• expert system design
• automating creation of computer programs for design circuits using genetic programming
• self-organizing maps
• knowledge-based techniques for software quality management
• object networks in developing intelligent systems
• application of genetic programming in software quality prediction
• neural networks for software quality prediction
• fuzzy Petri nets and Choquet integral in software cost estimation
• non-Cartesian approach to software development
• inductive programming.
• (source: Nielsen Book Data)

This unique volume is the first publication on software engineering and computational intelligence (CI) viewed as a synergistic interplay of neurocomputing, granular computation (including fuzzy sets and rough sets), and evolutionary methods. It presents a unified view of CI in the context of software engineering.The book addresses a number of crucial issues: what is CI, what role does it play in software development, how are CI elements built into successive phases of the software life cycle, and what is the role played by CI in quantifying fundamental features of software artifacts?With contributions from leading researchers and practitioners, the book provides the reader with a wealth of new concepts and approaches, complete algorithms, in-depth case studies, and thought-provoking exercises. The topics coverage include neurocomputing, granular as well as evolutionary computing, object-oriented analysis and design in software engineering. There is also an extensive bibliography.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Probabilistic inference in graphical models
• 3. Pattern classification
• 4. Unsupervised learning
• 5. Fata compression
• 6. Vhannel coding
• 7. Future research directions.

A variety of problems in machine learning and digital communication deal with complex but structured natural or artificial systems. In this book, Brendan Frey uses graphical models as an overarching framework to describe and solve problems of pattern classification, unsupervised learning, data compression, and channel coding. Using probabilistic structures such as Bayesian belief networks and Markov random fields, he is able to describe the relationships between random variables in these systems and to apply graph-based inference techniques to develop new algorithms. Among the algorithms described are the wake-sleep algorithm for unsupervised learning, the iterative turbodecoding algorithm (currently the best error-correcting decoding algorithm), the bits-back coding method, the Markov chain Monte Carlo technique, and variational inference.
(source: Nielsen Book Data)
A variety of problems in machine learning and digital communication deal with complex but structured natural or artificial systems. In this book, Brendan J. Frey uses graphical models as an overarching framework to describe and solve problems of pattern classification, unsupervised learning, data compression, and channel coding. Using probabilistic structures such as Bayesian belief networks and Markov random fields, he is able to describe the relationships between random variables in these systems and to apply graph-based inference techniques to develop new algorithms.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Probabilistic inference in graphical models
• 3. Pattern classification
• 4. Unsupervised learning
• 5. Fata compression
• 6. Vhannel coding
• 7. Future research directions.

A variety of problems in machine learning and digital communication deal with complex but structured natural or artificial systems. In this book, Brendan Frey uses graphical models as an overarching framework to describe and solve problems of pattern classification, unsupervised learning, data compression, and channel coding. Using probabilistic structures such as Bayesian belief networks and Markov random fields, he is able to describe the relationships between random variables in these systems and to apply graph-based inference techniques to develop algorithms. Among the algorithms described are the wake-sleep algorithm for unsupervised learning, the iterative turbodecoding algorithm, the bits-back coding method, the Markov chain Monte Carlo technique, and variational inference.
(source: Nielsen Book Data)
A variety of problems in machine learning and digital communication deal with complex but structured natural or artificial systems. In this book, Brendan J. Frey uses graphical models as an overarching framework to describe and solve problems of pattern classification, unsupervised learning, data compression, and channel coding. Using probabilistic structures such as Bayesian belief networks and Markov random fields, he is able to describe the relationships between random variables in these systems and to apply graph-based inference techniques to develop new algorithms.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Probabilistic inference in graphical models
• 3. Pattern classification
• 4. Unsupervised learning
• 5. Fata compression
• 6. Vhannel coding
• 7. Future research directions.

A variety of problems in machine learning and digital communication deal with complex but structured natural or artificial systems. In this book, Brendan Frey uses graphical models as an overarching framework to describe and solve problems of pattern classification, unsupervised learning, data compression, and channel coding. Using probabilistic structures such as Bayesian belief networks and Markov random fields, he is able to describe the relationships between random variables in these systems and to apply graph-based inference techniques to develop algorithms. Among the algorithms described are the wake-sleep algorithm for unsupervised learning, the iterative turbodecoding algorithm, the bits-back coding method, the Markov chain Monte Carlo technique, and variational inference.
(source: Nielsen Book Data)
A variety of problems in machine learning and digital communication deal with complex but structured natural or artificial systems. In this book, Brendan J. Frey uses graphical models as an overarching framework to describe and solve problems of pattern classification, unsupervised learning, data compression, and channel coding. Using probabilistic structures such as Bayesian belief networks and Markov random fields, he is able to describe the relationships between random variables in these systems and to apply graph-based inference techniques to develop new algorithms.
(source: Nielsen Book Data)

• Bianchini, Frasconi, Gori, and Maggini, Optimal Learning in Artificial Neural Networks: A Theoretical View. Kanjilal, Orthogonal Transformation Techniques in the Optimization of Feedforward Neural Network Systems. Museli, Sequential Constructive Techniques. Yu, Xu, and Wang, Fast Backpropagation Training Using Optimal Learning Rate and Momentum. Angulo and Torras, Learning of Nonstationary Processes. Schaller, Constraint Satisfaction Problems. Yang and Chen, Dominant Neuron Techniques. Lin, Chiang, and Kim, CMAC-based Techniques for Adaptive Learning Control. Deco, Information Dynamics and Neural Techniques for Data Analysis. Gorinevsky, Radial Basis Function Network Approximation and Learning in Task-Dependent Feedforward Control of Nonlinear Dynamical Systems.
• (source: Nielsen Book Data)

This volume covers practical and effective implementation techniques, including recurrent methods, Boltzmann machines, constructive learning with methods for the reduction of complexity in neural network systems, modular systems, associative memory, neural network design based on the concept of the Inductive Logic Unit, and a comprehensive treatment of implementations in the area of data classification. Numerous examples enhance the text. Practitioners, researchers, and students in engineering and computer science will find Implementation Techniques a comprehensive and powerful reference.
(source: Nielsen Book Data)

• Albertini and Pra, Recurrent Neural Networks: Identification and Other System Theoretic Properties. Anderson and Titterington, Boltzmann Machines: Statistical Associations and Algorithms for Training Anderson and Titterington. Campbell, Constructive Learning Techniques for Designing Neural Network Systems. Mehrotra and Mohan, Modular Neural Networks. Xu and Kwong, Associative Memories. Fry and Sova, A Logical Basis for Neural Network Design.Hoekstra, Duin, and Kraaijveld, Neural Networks Applied to Data Analysis. Zhang and Wang, Multi-Mode Single Neuron Arithmetics.
• (source: Nielsen Book Data)

Optimization Techniques is a unique reference source to a diverse array of methods for achieving optimization, and includes both systems structures and computational methods. The text devotes broad coverage toa unified view of optimal learning, orthogonal transformation techniques, sequential constructive techniques, fast back propagation algorithms, techniques for neural networks with nonstationary or dynamic outputs, applications to constraint satisfaction, optimization issues and techniques for unsupervised learning neural networks, optimum Cerebellar Model of Articulation Controller systems, a new statistical theory of optimum neural learning, and the role of the Radial Basis Function in nonlinear dynamical systems.This volume is useful for practitioners, researchers, and students in industrial, manufacturing, mechanical, electrical, and computer engineering.
(source: Nielsen Book Data)

• Contributors
• Acknowledgments
• 1. An Introduction to the Third Volume / Lee Spector, William B. Langdon, Una-May O'Reilly, Peter J. Angelino
• I. Applications
• 2. An Automatic Software Re-Engineering Tool Based on Genetic Programming / Conor Ryan and Laur Ivan
• 3. CAD Surface Reconstruction from Digitized 3D Point Data with a Genetic Programming/Evolution Strategy Hybrid / Robert E. Keller, Wolfgang Banzhaf, Jorn Mehnen and Klaus Weinert
• 4. A Genetic Programming Approach for Robust Language Interpretation / Carolyn Penstein Rose
• 5. Time Series Modeling Using Genetic Programming: An Application to Rainfall-Runoff Models / Peter A. Whigham and Peter F. Crapper
• 6. Automatic Synthesis, Placement, and Routing of Electrical Circuits by Means of Genetic Programming / John R. Koza and Forest H. Bennett III
• 7. Quantum Computing Applications of Genetic Programming / Lee Spector, Howard Barnum, Herbert J. Bernstein and Nikhil Swamy
• II. Theory
• II. Theory
• 8. The Evolution of Size and Shape / William B. Langdon, Terry Soule, Riccardo Poli and James A. Foster
• 9. Fitness Distributions: Tools for Designing Efficient Evolutionary Computations / Christian Igel and Kumar Chellapilla
• 10. Analysis of Single-Node (Building) Blocks in Genetic Programming / Jason M. Daida, Robert R. Bertram, John A. Polito 2 and Stephen A. Stanhope
• 11. Rooted-Tree Schemata in Genetic Programming / Justinian P. Rosca and Dana H. Ballard
• III. Extensions
• III. Extensions
• 12. Efficient Evolution of Machine Code for CISC Architectures Using Instruction Blocks and Homologous Crossover / Peter Nordin, Wolfgang Banzhaf and Frank D. Francone
• 13. Sub-machine-code Genetic Programming / Riccardo Poli and William B. Langdon
• 14. The Internal Reinforcement of Evolving Algorithms / Astro Teller
• 15. Inductive Genetic Programming with Immune Network Dynamics / Nikolay I. Nikolaev, Hitoshi Iba and Vanio Slavov
• 16. A Self-Tuning Mechanism for Depth-Dependent Crossover / Takuyo Ito, Hitoshi Iba and Satoshi Sato
• 17. Genetic Recursive Regression for Modeling and Forecasting Real-World Chaotic Time Series / Geum Yong Lee
• 18. Co-evolutionary Fitness Switching: Learning Complex Collective Behaviors Using Genetic Programming / Byoung-Tak Zhang and Dong-Yeon Cho
• 19. Evolving Multiple Agents by Genetic Programming / Hitoshi Iba
• Index.

Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time-and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of Advances in Genetic Programming highlights many of the recent technical advances in this increasingly popular field. Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time-and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of Advances in Genetic Programming highlights many of the recent technical advances in this increasingly popular field.
(source: Nielsen Book Data)
Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time -- and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of "Advances in Genetic Programming" highlights many of the recent technical advances in this increasingly popular field.
(source: Nielsen Book Data)

• Introduction to support vector learning
• roadmap. Part 1 Theory: three remarks on the support vector method of function estimation, Vladimir Vapnik
• generalization performance of support vector machines and other pattern classifiers, Peter Bartlett and John Shawe-Taylor
• Bayesian voting schemes and large margin classifiers, Nello Cristianini and John Shawe-Taylor
• support vector machines, reproducing kernel Hilbert spaces, and randomized GACV, Grace Wahba
• geometry and invariance in kernel based methods, Christopher J.C. Burges
• on the annealed VC entropy for margin classifiers - a statistical mechanics study, Manfred Opper
• entropy numbers, operators and support vector kernels, Robert C. Williamson et al. Part 2 Implementations: solving the quadratic programming problem arising in support vector classification, Linda Kaufman
• making large-scale support vector machine learning practical, Thorsten Joachims
• fast training of support vector machines using sequential minimal optimization, John C. Platt. Part 3 Applications: support vector machines for dynamic reconstruction of a chaotic system, Davide Mattera and Simon Haykin
• using support vector machines for time series prediction, Klaus-Robert Muller et al
• pairwise classification and support vector machines, Ulrich Kressel. Part 4 Extensions of the algorithm: reducing the run-time complexity in support vector machines, Edgar E. Osuna and Federico Girosi
• support vector regression with ANOVA decomposition kernels, Mark O. Stitson et al
• support vector density estimation, Jason Weston et al
• combining support vector and mathematical programming methods for classification, Bernhard Scholkopf et al.
• (source: Nielsen Book Data)

The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university researchers and engineers developing applications for the corporate world, form a Who's Who of this exciting new area.ContributorsPeter Bartlett, Kristin P. Bennett, Christopher J.C. Burges, Nello Cristianini, Alex Gammerman, Federico Girosi, Simon Haykin, Thorsten Joachims, Linda Kaufman, Jens Kohlmorgen, Ulrich Kressel, Davide Mattera, Klaus-Robert Muller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar Ratsch, Bernhard Schoelkopf, John Shawe-Taylor, Alexander J. Smola, Mark O. Stitson, Vladimir Vapnik, Volodya Vovk, Grace Wahba, Chris Watkins, Jason Weston, Robert C. Williamson.
(source: Nielsen Book Data)
The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university researchers and engineers developing applications for the corporate world, form a Who's Who of this exciting new area.
(source: Nielsen Book Data)

• Introduction - background material
• the building blocks
• the April++ language - AprilO - adding objects to April
• AprilQ - the database extension
• April++ - objects with knowledge base state
• the implementation of April++
• the applications - component based agent construction
• an agent for multi-service network management.
• (source: Nielsen Book Data)

Advances in computer technology in general and computer networks in particular have significantly affected the requirements of modern applications, where the need to operate in decentralised environments is of primary importance. The conceptual models of the applications are also becoming complex and semantically rich.A promising technology towards the design and development of systems of such domains is agent based systems. Agents, having a knowledge component, act and interact with other agents and information sources in order to achieve some goals. Platforms intended for supporting the development of such systems should offer a number of features, including communication, concurrency, mobility, high level data structures, object orientation etc.This book describes the design and implementation of such a language platform called April++ and its use on a number of applications. Methodologically, in designing and implementing the language, a layered approach has been adopted. April++ has been developed as a series of macro defined layers on top of the relatively primitive features of a pre-existing language called April.On top of April++, an agent layer has been built (as a set of pre-defined classes) for constructing agent based systems. This layer has been customised for specific application domains considered. This includes a mobile agent application, a distributed database application and a network management one.
(source: Nielsen Book Data)

• Introduction - background material
• the building blocks
• the April++ language - AprilO - adding objects to April
• AprilQ - the database extension
• April++ - objects with knowledge base state
• the implementation of April++
• the applications - component based agent construction
• an agent for multi-service network management.
• (source: Nielsen Book Data)

Advances in computer technology in general and computer networks in particular have significantly affected the requirements of modern applications, where the need to operate in decentralised environments is of primary importance. The conceptual models of the applications are also becoming complex and semantically rich.A promising technology towards the design and development of systems of such domains is agent based systems. Agents, having a knowledge component, act and interact with other agents and information sources in order to achieve some goals. Platforms intended for supporting the development of such systems should offer a number of features, including communication, concurrency, mobility, high level data structures, object orientation etc.This book describes the design and implementation of such a language platform called April++ and its use on a number of applications. Methodologically, in designing and implementing the language, a layered approach has been adopted. April++ has been developed as a series of macro defined layers on top of the relatively primitive features of a pre-existing language called April.On top of April++, an agent layer has been built (as a set of pre-defined classes) for constructing agent based systems. This layer has been customised for specific application domains considered. This includes a mobile agent application, a distributed database application and a network management one.
(source: Nielsen Book Data)

• Machine generated contents note: Open Sesame?: Fifteen Variations on the Theme of Openness in Learning Environments / J. Self
• Cognitive Applications of New Computational Technologies in Eye Tracking / S.P. Marshall
• Collaborative Learning in Open Distributed Environments -- Pedagogical Principles and Computational Methods / H.U. Hoppe
• Overview of the State of the Art in ITS Authoring Tools / T. Murray
• Trends and Issues in AI and Education: Towards a Common Research Framework / J. Sandberg
• Agent Systems for Diversity in Human Learning / J. Les / G. Cumming / S. Finch
• Teachable Agents: Combining Insights from Learning Theory and Computer Science / S. Brophy / G. Biswas / T. Katzlberger / [and others]
• Meta-knowledge Representation for Learning Scenarios Engineering / G. Paquette
• Multi-Agent Design of a Peer-Help Environment / J. Vassileva / J. Greer / G. McCalla / [and others]
• Methodology for Building Intelligent Educational Agents / H.N. Keeling
• Systemion: A New Agent Model to Design Intelligent Tutoring Systems / M.F. Canut / G. Gouarderes / E. Sanchis
• Learning Goal Ontology Supported by Learning Theories for Opportunistic Group Formation / T. Supnithi / A. Inaba / M. Ikeda / [and others]
• Toward Intelligent Analysis and Support of Collaborative Learning Interaction / A. Soller / F. Linton / B. Goodman / [and others]
• Ontology-Aware Authoring Tool: Functional Structure and Guidance Generation / L. Jin / W. Chen / Y. Hayashi / [and others]
• Formatively Evaluating REDEEM -- An Authoring Environment for ITSs / S. Ainsworth / J. Underwood / S. Grimshaw
• Intelligent Agent Instructional Design Tool for a Hypermedia Design Course / S. Stoyanov / L. Aroyo / P. Kommers.

Annotation This volume will be the proceedings for the 9th International Conference on Artificial Intelligence in Education (AI-ED 1999). This is one of a series of international conferences in this area and it is designed to report on state of the art research in the field of AI in Education. This field is interdisciplinary and brings together researchers from the domains of computer science, cognitive science, education, psychology, linguistics, and engineering (to name a few). Researchers will contribute manuscripts to this volume, providing opportunities for cross-fertilization of information across disciplines that will lead to advances and new applications in the field. New computational technologies will be described that support learning, exploration, and collaboration. Technologies can support both the individual and small groups of individuals who work collaboratively to solve problems and learn new materials. New methodologies for supporting and documenting the learning process in both these situations are being developed and are described in this volume.

• pt. I. Technology. Robust layered control system for a mobile robot
• Robot that walks: emergent behaviors from a carefully evolved network
• Learning a distributed map representation based on navigation behaviors
• New approaches to robotics. pt. II. Philosophy. Intelligence without representation
• Planning is just a way of avoiding figuring out what to do next
• Elephants don't play chess
• Intelligence without reason.

Until the mid-1980s, AI researchers assumed that an intelligent system doing high-level reasoning was necessary for the coupling of perception and action. In this traditional model, cognition mediates between perception and plans of action. Realizing that this core AI, as it was known, was illusory, Rodney A. Brooks turned the field of AI on its head by introducing the behavior-based approach to robotics. The cornerstone of behavior-based robotics is the realization that the coupling of perception and action gives rise to all the power of intelligence and that cognition is only in the eye of an observer. Behavior-based robotics has been the basis of successful applications in entertainment, service industries, agriculture, mining, and the home. It has given rise to both autonomous mobile robots and more recent humanoid robots such as Brooks' Cog. This book represents Brooks' initial formulation of and contributions to the development of the behavior-based approach to robotics. It presents all of the key philosophical and technical ideas that put this "bottom-up" approach at the forefront of current research in not only AI but all of cognitive science.
(source: Nielsen Book Data)

• An introduction to evolutionary computation
• evolutionary algorithms as search algorithms
• theoretical analysis of evolutionary algorithms
• advanced search operators in evolutionary algorithms
• parallel evolutionary algorithms
• a comparison of simulated annealing and an evolutionary algorithm on traveling salesman problems
• power system design and management by evolutionary algorithms
• telecommunications network design and management by evolutionary algorithms
• an optimization tool based on evolutionary algorithms
• the evolution of artificial neural network architectures
• an experimental study of generalization in evolutionary learning
• an evolutionary approach to the N-person prisoner's dilemma game
• automated design and generalisation of heuristics
• high-order credit assignment in classifier systems.
• (source: Nielsen Book Data)

Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. This book is devoted to the theory and application of evolutionary computation. It is a self-contained volume which covers both introductory material and selected advanced topics. The book can roughly be divided into two major parts: the introductory one and the one on selected advanced topics. Each part consists of several chapters which present an in-depth discussion of selected topics. A strong connection is established between evolutionary algorithms and traditional search algorithms. This connection enables us to incorporate ideas in more established fields into evolutionary algorithms. The book is aimed at a wide range of readers. It does not require previous exposure to the field since introductory material is included. It will be of interest to anyone who is interested in adaptive optimization and learning. People in computer science, artificial intelligence, operations research, and various engineering fields will find it particularly interesting.
(source: Nielsen Book Data)

• Cortical images, self-organising neural networks and object classification / Nikolay Petkov
• Parallel implementation of a neural network ensemble on the connection machine / Daijin Kim, Minsoo Suk
• Boolean neural networks trained with simulated annealing / Jarkko Niittylahti
• On the computational complexity of analyzing the Hopfield-Clique network / Arun Jagota
• A harmony-maximisation network implementation of a compound labeling scheme for scene analysis / Tatiana Tambouratzis
• Optimal image boundary via Hopfield net and tunneling / William Cheung, Roland Chin, Tong Lee
• Shape matching based on invariants / Stan Z. Li.

• Neural networks in systems identification and control - supervised learning in multilayer perceptions - the back-propagation algorithm
• identification of two-dimensional state space discrete systems using neural networks
• neural networks for control
• neuro-based adaptive regulator
• local model networks and self-tuning predictive control
• fuzzy and neuro-fuzzy systems in modelling, control and robot path planning - an on-line self constructing fuzzy modelling architecture based on neural and fuzzy concepts and techniques
• neuro-fuzzy model-based control
• fuzzy and neurofuzzy approaches to mobile robot path and motion planning under uncertainty
• genetic-evolutionary algorithms - a tutorial overview of genetic algorithms and their applications
• results from a variety of genetic algorithm applications showing the robustness of the approach
• evolutionary algorithms in computer-aided design of integrated circuits
• soft computing applications - soft data fusion
• application of neural networks to computer gaming
• coherent neural networks and their applications to control and signal processing
• neural, fuzzy and evolutionary reinforcement learning systems - an application case study
• neural networks in industrial and environmental applications.
• (source: Nielsen Book Data)

Soft computing is a branch of computing which, unlike hard computing, can deal with uncertain, imprecise and inexact data. The three constituents of soft computing are fuzzy-logic-based computing, neurocomputing, and genetic algorithms. Fuzzy logic contributes the capability of approximate reasoning, neurocomputing offers function approximation and learning capabilities, and genetic algorithms provide a methodology for systematic random search and optimization. These three capabilities are combined in a complementary and synergetic fashion.This book presents a cohesive set of contributions dealing with important issues and applications of soft computing in systems and control technology. The contributions include state-of-the-art material, mathematical developments, fresh results, and how-to-do issues. Among the problems studied via neural, fuzzy, neurofuzzy and genetic methodologies are: data fusion, reinforcement learning, approximation properties, multichannel imaging, signal processing, system optimization, gaming, and several forms of control.The book can serve as a reference for researchers and practitioners in the field. Readers can find in it a large amount of useful and timely information, and thus save considerable effort in searching for other scattered literature.
(source: Nielsen Book Data)

• Ch. 1. Introduction
• Ch. 2. Ant Foraging Behavior, Combinatorial Optimization, and Routing in Communications Network
• Ch. 3. Division of Labor and Task Allocation
• Ch. 4. Cemetery Organization, Brood Sorting, Data Analysis, and Graph Partitioning
• Ch. 5. Self-Organization and Templates: Application to Data Analysis and Graph Partitioning
• Ch. 6. Nest Building and Self-Assembling
• Ch. 7. Cooperative Transport by Insects and Robots
• Ch. 8. Epilogue.

Social insects-ants, bees, termites, and wasps-can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Composed of simple interacting agents, this intelligence lies in the networks of interactions among individuals and between individuals and the environment. A fascinating subject, social insects are also a powerful metaphor for artificial intelligence, and the problems they solve-finding food, dividing labor among nestmates, building nests, responding to external challenges-have important counterparts in engineering and computer science. This book provides a detailed look at models of social insect behaviour and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.
(source: Nielsen Book Data)

• The Study of Intelligence--Foundations and Issues
• The Study of Intelligence
• Characterizing Intelligence
• Studying Intelligence: The Synthetic Approach
• Foundations of Classical Artificial Intelligence and Cognitive Science
• Cognitive Science: Preliminaries
• The Cognitivistic Paradigm
• An Architecture for an Intelligent Agent
• The Fundamental Problems of Classical Al and Cognitive Science
• Real Worlds versus Virtual Worlds
• Some Well-Known Problems with Classical Systems
• The Fundamental Problems of Classical Al
• Remedies and Alternatives
• A Framework for Embodied Cognitive Science
• Embodied Cognitive Science: Basic Concepts
• Complete Autonomous Agents
• Biological and Artificial Agents
• Designing for Emergence--Logic-Based and Embodied Systems
• Explaining Behavior
• Neural Networks for Adaptive Behavior
• From Biological to Artificial Neural Networks
• The Four or Five Basics
• Distributed Adaptive Control
• Types of Neural Networks
• Beyond Information Processing: A Polemic Digression
• Approaches and Agent Examples
• Braitenberg Vehicles
• Motivation
• The Fourteen Vehicles
• Segmentation of Behavior and the Extended Braitenberg Architecture
• The Subsumption Architecture
• Behavior-Based Robotics
• Designing a Subsumption-Based Robot
• Examples of Subsumption-Based Architectures
• Conclusions: The Subsumption Approach to Designing Intelligent Systems
• Artificial Evolution and Artificial Life.

By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run "programs"; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior -- thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI, " and "behavior-based AI." This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building. The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.
(source: Nielsen Book Data)
Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control behaviour and ensure survival. Researchers agree that intelligence always manifests itself in behaviour - thus it is behaviour that must be understood. A new field has grown around the study of behaviour-based intelligence, also known as embodied cognitive science, "new AI" and "behaviour-based AI". This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization and learning, the authors derive a set of principles and a framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building. The text includes the background material required to understand the principles underlying intelligence, as well as information of intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.
(source: Nielsen Book Data)

• The Study of Intelligence--Foundations and Issues
• The Study of Intelligence
• Characterizing Intelligence
• Studying Intelligence: The Synthetic Approach
• Foundations of Classical Artificial Intelligence and Cognitive Science
• Cognitive Science: Preliminaries
• The Cognitivistic Paradigm
• An Architecture for an Intelligent Agent
• The Fundamental Problems of Classical Al and Cognitive Science
• Real Worlds versus Virtual Worlds
• Some Well-Known Problems with Classical Systems
• The Fundamental Problems of Classical Al
• Remedies and Alternatives
• A Framework for Embodied Cognitive Science
• Embodied Cognitive Science: Basic Concepts
• Complete Autonomous Agents
• Biological and Artificial Agents
• Designing for Emergence--Logic-Based and Embodied Systems
• Explaining Behavior
• Neural Networks for Adaptive Behavior
• From Biological to Artificial Neural Networks
• The Four or Five Basics
• Distributed Adaptive Control
• Types of Neural Networks
• Beyond Information Processing: A Polemic Digression
• Approaches and Agent Examples
• Braitenberg Vehicles
• Motivation
• The Fourteen Vehicles
• Segmentation of Behavior and the Extended Braitenberg Architecture
• The Subsumption Architecture
• Behavior-Based Robotics
• Designing a Subsumption-Based Robot
• Examples of Subsumption-Based Architectures
• Conclusions: The Subsumption Approach to Designing Intelligent Systems
• Artificial Evolution and Artificial Life.

Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control behaviour and ensure survival. Researchers agree that intelligence always manifests itself in behaviour - thus it is behaviour that must be understood. A new field has grown around the study of behaviour-based intelligence, also known as embodied cognitive science, "new AI" and "behaviour-based AI". This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization and learning, the authors derive a set of principles and a framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building. The text includes the background material required to understand the principles underlying intelligence, as well as information of intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.
(source: Nielsen Book Data)
By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run "programs"; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior--thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI, " and "behavior-based AI."This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building.The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.
(source: Nielsen Book Data)

• The Study of Intelligence--Foundations and Issues
• The Study of Intelligence
• Characterizing Intelligence
• Studying Intelligence: The Synthetic Approach
• Foundations of Classical Artificial Intelligence and Cognitive Science
• Cognitive Science: Preliminaries
• The Cognitivistic Paradigm
• An Architecture for an Intelligent Agent
• The Fundamental Problems of Classical Al and Cognitive Science
• Real Worlds versus Virtual Worlds
• Some Well-Known Problems with Classical Systems
• The Fundamental Problems of Classical Al
• Remedies and Alternatives
• A Framework for Embodied Cognitive Science
• Embodied Cognitive Science: Basic Concepts
• Complete Autonomous Agents
• Biological and Artificial Agents
• Designing for Emergence--Logic-Based and Embodied Systems
• Explaining Behavior
• Neural Networks for Adaptive Behavior
• From Biological to Artificial Neural Networks
• The Four or Five Basics
• Distributed Adaptive Control
• Types of Neural Networks
• Beyond Information Processing: A Polemic Digression
• Approaches and Agent Examples
• Braitenberg Vehicles
• Motivation
• The Fourteen Vehicles
• Segmentation of Behavior and the Extended Braitenberg Architecture
• The Subsumption Architecture
• Behavior-Based Robotics
• Designing a Subsumption-Based Robot
• Examples of Subsumption-Based Architectures
• Conclusions: The Subsumption Approach to Designing Intelligent Systems
• Artificial Evolution and Artificial Life.

Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control behaviour and ensure survival. Researchers agree that intelligence always manifests itself in behaviour - thus it is behaviour that must be understood. A new field has grown around the study of behaviour-based intelligence, also known as embodied cognitive science, "new AI" and "behaviour-based AI". This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization and learning, the authors derive a set of principles and a framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building. The text includes the background material required to understand the principles underlying intelligence, as well as information of intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.
(source: Nielsen Book Data)
By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run "programs"; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior--thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI, " and "behavior-based AI."This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building.The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.
(source: Nielsen Book Data)

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification-that is, a scale parameter-rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
(source: Nielsen Book Data)

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification-that is, a scale parameter-rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
(source: Nielsen Book Data)

The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification-that is, a scale parameter-rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
(source: Nielsen Book Data)

• 1. Dimensions
• 2. Symbolic, conceptual, and subconceptual representations
• 3. Properties
• 4. Concepts
• 5. Semantics
• 6. Induction
• 7. Computational aspects
• 8. In Chase of Space.

Within cognitive science, two approaches currently dominate the problem of modelling representations. The symbolic approach views cognition as computation involving symbolic manipulation. Connectionism, a special case of associationism, models associations using artificial neuron networks. Peter Gardenfors offers his theory of conceptual representations as a bridge between the symbolic and connectionist approaches. Symbolic representation is particularly weak at modeling concept learning, which is paramount for understanding many cognitive phenomena. Concept learning is closely tied to the notion of similarity, which is also poorly served by the symbolic approach. Gardenfors's theory of conceptual spaces presents a framework for representing information on the conceptual level. A conceptual space is built up from geometrical structures based on a number of quality dimensions. The main applications of the theory are on the constructive side of cognitive science: as a constructive model the theory can be applied to the development of artificial systems capable of solving cognitive tasks. Gardenfors also shows how conceptual spaces can serve as an explanatory framework for a number of empirical theories, in particular those concerning concept formation, induction and semantics. His aim is to present a coherent research program that can be used as a basis for more detailed investigations.
(source: Nielsen Book Data)

• I. Introduction
• 1. Nudge Nudge Wink Wink: Elements of Face-to-Face Conversation for Embodied Conversation Agents / Justine Cassell
• II. Systems
• 2. Human Conversation as a System Framework: Designing Embodied Conversational Agents / Justine Cassell, Tim Bickmore and Lee Campbell / [and others]
• 3. "May I Help You?": Designing Embodied Conversational Agent Allies / Elizabeth F. Churchill, Linda Cook and Peter Hodgson / [and others]
• 4. Task-Oriented Collaboration with Embodied Agents in Virtual Worlds / Jeff Rickel and W. Lewis Johnson
• 5. Deictic and Emotive Communication in Animated Pedagogical Agents / James C. Lester, Stuart G. Towns and Charles B. Callaway / [and others]
• 6. Performative Facial Expressions in Animated Faces / Isabella Poggi and Catherine Pelachaud
• 7. Emotion and Personality in a Conversational Agent / Gene Ball and Jack Breese
• 8. The Automated Design of Believable Dialogues for Animated Presentation Teams / Elisabeth Andre, Thomas Rist and Susanne van Mulken / [and others]
• 9. Parameterized Action Representation for Virtual Human Agents / Norman I. Badler, Rama Bindiganavale and Jan Allbeck / [and others]
• III. Evaluation
• 10. Developing and Evaluating Conversational Agents / Dominic W. Massaro, Michael M. Cohen and Jonas Beskow / [and others]
• 11. Designing and Evaluating Conversational Interfaces with Animated Characters / Sharon Oviatt and Bridget Adams
• 12. Measurement and Evaluation of Embodied Conversational Agents / Greogry A. Sanders and Jean Scholtz
• 13. Truth Is Beauty: Researching Embodied Conversational Agents / Clifford Nass, Katherine Isbister and Eun-Ju Lee.

This book describes research in all aspects of the design, implementation, and evaluation of embodied conversational agents as well as details of specific working systems. Embodied conversational agents are computer-generated cartoonlike characters that demonstrate many of the same properties as humans in face-to-face conversation, including the ability to produce and respond to verbal and nonverbal communication. They constitute a type of (a) multimodal interface where the modalities are those natural to human conversation: speech, facial displays, hand gestures, and body stance; (b) software agent, insofar as they represent the computer in an interaction with a human or represent their human users in a computational environment (as avatars, for example); and (c) dialogue system where both verbal and nonverbal devices advance and regulate the dialogue between the user and the computer. With an embodied conversational agent, the visual dimension of interacting with an animated character on a screen plays an intrinsic role. Not just pretty pictures, the graphics display visual features of conversation in the same way that the face and hands do in face-to-face conversation among humans. This book describes research in all aspects of the design, implementation, and evaluation of embodied conversational agents as well as details of specific working systems. Many of the chapters are written by multidisciplinary teams of psychologists, linguists, computer scientists, artists, and researchers in interface design. The authors include Elisabeth Andre, Norm Badler, Gene Ball, Justine Cassell, Elizabeth Churchill, James Lester, Dominic Massaro, Cliff Nass, Sharon Oviatt, Isabella Poggi, Jeff Rickel, and Greg Sanders.
(source: Nielsen Book Data)

• 1. Introduction
• 2. IMPACT architecture
• 3. service description language
• 4. Accessing legacy data and software
• 5. IMPACT server implementation
• 6. Agent programs
• 7. Meta agent programs
• 8. Temporal agent programs
• 9. Probabilistic agent programs
• 10. Secure agent programs
• 11. Complexity results
• 12. Implementing agents
• 13. An example application
• 14. Conclusions.

Software agents are the latest advance in the trend toward smaller, modular pieces of code, where each module performs a well-defined, focused task or set of tasks. Programmed to interact with and provide services to other agents, including humans, software agents act autonomously with prescribed backgrounds, beliefs, and operations. Systems of agents can access and manipulate heterogeneously stored data such as that found on the Internet. After a discussion of the theory of software agents, this book presents IMPACT (Interactive Maryland Platform for Agents Collaborating Together), an experimental agent infrastructure that translates formal theories of agency into a functional multiagent system that can extend legacy software code and application-specific or legacy data structures. The book describes three sample applications: a store, a self-correcting auto-pilot, and a supply chain.
(source: Nielsen Book Data)

• Preface
• 1. The learning methodology
• 2. Linear learning machines
• 3. Kernel-induced feature spaces
• 4. Generalisation theory
• 5. Optimisation theory
• 6. Support vector machines
• 7. Implementation techniques
• 8. Applications of support vector machines
• Appendix A: pseudocode for the SMO algorithm
• Appendix B: background mathematics
• Appendix C: glossary
• Appendix D: notation
• Bibliography
• Index.
• (source: Nielsen Book Data)

This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.
(source: Nielsen Book Data)

• Introduction
• Substrate systems
• Team member agent architecture
• Layered learning
• Learning an individual skill
• Learning a multiagent behavior
• Learning a team behavior
• Competition results
• Related work
• Conclusions and future work.

This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. The book makes four main contributions to the fields of machine learning and multiagent systems. First, it describes an architecture within which a flexible team structure allows member agents to decompose a task into flexible roles and to switch roles while acting. Second, it presents layered learning, a general-purpose machine-learning method for complex domains in which learning a mapping directly from agents' sensors to their actuators is intractable with existing machine-learning methods. Third, the book introduces a new multiagent reinforcement learning algorithm-team-partitioned, opaque-transition reinforcement learning (TPOT-RL)-designed for domains in which agents cannot necessarily observe the state-changes caused by other agents' actions. The final contribution is a fully functioning multiagent system that incorporates learning in a real-time, noisy domain with teammates and adversaries-a computer-simulated robotic soccer team. Peter Stone's work is the basis for the CMUnited Robotic Soccer Team, which has dominated recent RoboCup competitions. RoboCup not only helps roboticists to prove their theories in a realistic situation, but has drawn considerable public and professional attention to the field of intelligent robotics. The CMUnited team won the 1999 Stockholm simulator competition, outscoring its opponents by the rather impressive cumulative score of 110-0.
(source: Nielsen Book Data)

• 1. Rational Agents
• 2. The Belief-Desire-Intention Model
• 3. Introduction to LORA
• 4. LORA Defined
• 5. Properties of Rational Agents
• 6. Collective Mental States
• 7. Communication
• 8. Cooperation
• 9. Logic and Agent Theory.

One goal of modern computer science is to engineer computer programs that can act as autonomous, rational agents; software that can independently make good decisions about what actions to perform on our behalf and execute those actions. Applications range from small programs that intelligently search the Web buying and selling goods via electronic commerce, to autonomous space probes. This book focuses on the belief-desire-intention (BDI) model of rational agents, which recognizes the primacy of beliefs, desires, and intentions in rational action. The BDI model has three distinct strengths: an underlying philosophy based on practical reasoning in humans, a software architecture that is implementable in real systems, and a family of logics that support a formal theory of rational agency.The book introduces a BDI logic called LORA (Logic of Rational Agents). In addition to the BDI component, LORA contains a temporal component, which allows one to represent the dynamics of how agents and their environments change over time, and an action component, which allows one to represent the actions that agents perform and the effects of the actions. The book shows how LORA can be used to capture many components of a theory of rational agency, including such notions as communication and cooperation.
(source: Nielsen Book Data)

• Outline of a computational theory of perceptions based on computing with words / L.A. Zadeh
• Introduction to soft computing and intelligent control systems / N.K. Sinha and M.M. Gupta
• Computational issues in intelligent control / X.D. Koutsoukos and P.J. Antsaklis
• Neural networks
• a guided tour / S. Haykin
• On generating variable structure organization using a genetic algorithm / A.K. Zaidi and A.H. Levis
• Evolutionary algorithms and neural networks / R.G.S. Asthana
• Neural networks and fuzzy systems / P. Musilek and M.M. Gupta
• Fuzzy neural networks / P. Musilek and M.M. Gupta
• A cursory look at parallel and distributed architectures and biologically inspired computing / S.K. Basu
• Developments in learning control systems / J.X. Xu [and others]
• Techniques for genetic adaptive control / W.K. Lennon and K.M. Passino
• Cooperative behavior of intelligent agents : theory and practice / L. Vlacic, A. Engwirda, and M. Kajitani
• Expert systems in process diagnosis and control / D. Popovic
• Neural networks for identification of nonlinear systems : an overview / P. Gupta and N.K. Sinha
• Sensor fusion system using recurrent fuzzy inference / F. Kobayashi [and others]
• Neurofuzzy state estimators / C.J. Harris, X. Hong, and Q. Gan.

• Soft computing paradigms for artificial vision / K.K. Shukla
• Intelligent control with neural networks / D. Popovic
• Knowledge-based adaptation of neurofuzzy models in predictive control of a heat exchanger / M. Fischer, O. Nelles, and R. Isermann
• Neural network approximation of piecewise continuous functions : application to friction compensation / R.R. Selmic and F.L. Lewis
• Fuzzy adaptive and predictive control of a thermic process / I. Skrjanc and D. Matko
• An intelligent approach to positive target identification / R.-N.P. Singh
• Adaptive agents and artificial life : insights for the power industry / S.A. Harp and T. Samad
• Truck backer-upper control using dynamic neural network / M.M Gupta and D.H. Rao
• Toward intelligent machines : future perspectives / M.M. Gupta and N.K. Sinha.

The field of soft computing is emerging from the cutting edge research over the last ten years devoted to fuzzy engineering and genetic algorithms. The subject is being called soft computing and computational intelligence. With acceptance of the research fundamentals in these important areas, the field is expanding into direct applications through engineering and systems science. This book cover the fundamentals of this emerging filed, as well as direct applications and case studies. There is a need for practicing engineers, computer scientists, and system scientists to directly apply "fuzzy" engineering into a wide array of devices and systems.
(source: Nielsen Book Data)

• Preface
• 1. The Machine That Could Learn Anything
• 2. Consider Thy Neighbor
• 3. Kepler on Mars
• 4. The Information Chicane
• 5. Fence-and-Fill Learning
• 6. Turing and the Submarines
• 7. The Relational Gulf
• 8. The Supercharged Learner
• 9. David Hume and the Crash of '87
• 10. Phases of Compression
• 11. Protorepresentational Learning
• 12. The Creativity Continuum
• References
• Index.

Chris Thornton makes the compelling claim that learning is not a passive discovery operation but an active process involving creativity on the part of the learner. This study of learning in autonomous agents offers a bracing intellectual adventure. Chris Thornton makes the compelling claim that learning is not a passive discovery operation but an active process involving creativity on the part of the learner. Although theorists of machine learning tell us that all learning methods contribute some form of bias and thus involve a degree of creativity, Thornton carries the idea much further. He describes an incremental process, recursive relational learning, in which the results of one learning step serve as the basis for the next. Very high-level recodings are then substantially the creative artifacts of the learner's own processing. Lower-level recodings are more "objective" in that their properties are more severely constrained by the source data. Thornton sees consciousness as a process at the outer fringe of relational learning, just prior to the onset of creativity. According to this view, we cannot assume consciousness to be an exclusively human phenomenon, but rather the expected feature of any cognitive mechanism able to engage in extended flights of relational learning. Thornton presents key background material in an entertaining manner, using extensive mental imagery and a minimum of mathematics. Anecdotes and dialogue add to the text's informality.
(source: Nielsen Book Data)

• 1. Speech hyphenation segmentation by means of blind source separation / Harold Szu and Charles Hsu
• 2. Higher-order moments based synthesis of supervised fourier demixing filter / Eiji Uchino, Noriaki Suetake, and Takeshi Yamakawa
• 3. Design and application of an acoustic database navigator for the intractive analysis of psychoacoustic sound archives and sound engineerint / Andreas Konig, Friedrich E. Blutner, Michael Eberhardt, and Robert Wenzel
• 4. Multilayer perceptron networks with adaptive centroid transformation / Mikko Lehtokangas
• 5. Identification and analysis for transiently evoked otoacoustic emission / Liming Li, Jinsen Lin, and Zhengguo Zhang
• 6. New reliability models based on imprecise probabilities / Lev V. Utkin and Sergey V. Gurov.

• 7. Multi-modular neural network for breast cancer detection / Huai Li and K.J. Ray Liu
• 8. Advanced neural nets for visual image communication / Harold Szu and Charles Hsu
• 9. Continuous valued techniques based on the Lagrangian method for the wire routing problem / Shakeel Ismail, Masahiro Nagamatu and Torao Yanaru
• 10. Chaotic neural networks for information processing / Charles Hsu and Harold Szu
• 11. Wavelet encoding for interactive genetic algorithm in emotional image retrieval / Joo-Young Lee and Sung-Bae Cho
• 12. Call admission control using interval arithmetic coulomb energy network / Won Don Lee, Kyunghee Lee and Hong-kee Kim.

This book presents worldwide outstanding research and recent progress in the applications of neural networks, fuzzy logic, chaos, independent component analysis, etc to fields related to speech recognition enhancement, supervised Fourier demixing noise elimination, acoustic databases, the human hearing system, cancer detection, image processing, and visual communications.
(source: Nielsen Book Data)

• 1. Evolutionary and neural synthesis of intelligent agents / by Karthik Balakrishnan and Vasant Honavar
• 2. Cellular encoding for interactive evolutionary robotics / by Frédéric Gruau and Kameel Quatramaran
• 3. The emergence of communication through synthetic evolution / by Bruce J. MacLennan
• 4. Optimization of classifiers using genetic algorithms / by J.J. Merelo, A. Prieto and F. Morán
• 5. Evolving neuro-controllers and sensors for artificial agents / by Karthik Balakrishnan and Vasant Honavar
• 6. Combined biological metaphors / by Egbert J.W. Boers and Ida G. Sprinkhuizen-Kuyper
• 7. Evolutionary neurogenesis applied to mobile robotics / by Oliver Michel
• 8. Development in neural networks / by Domenico Parisi and Stefano Nolfi
• 9. Evolution and learning in radial basis function neural networks : a hybrid approach / by Brian Carse, Terence C. Fogarty and John C.W. Sullivan
• 10. Co-evolution and ontogenetic change in competing robots / by Dario Floreano, Stefano Nolfi and Francesco Mondada
• 11. Goal directed adaptive behavior in second-order neural networks : learning and evolving in the MAXSON architecture / by Federick L. Crabbe and Michael G. Dyer
• 12. Evolving heterogeneous neural agents by local selection / by Filippo Menczer, W. Nick Street and Melania Degeratu
• 13. Learning sequential decision tasks through symbiotic evolution of neural networks / by David E. Moriarty and Risto Miikkulainen
• 14. From evolving a single neural network to evolving neural network ensembles / by Xin Yao and Yong Liu
• 15. Evolutionary synthesis of Bayesian networks for optimization / by Heinz Mühlenbein and Thilo Mahnig.

This book explores a central issue in artificial intelligence, cognitive science, and artificial life: how to design information structures and processes that create and adapt intelligent agents through evolution and learning. Among the first uses of the computer was the development of programs to model perception, reasoning, learning, and evolution. Further developments resulted in computers and programs that exhibit aspects of intelligent behavior. The field of artificial intelligence is based on the premise that thought processes can be computationally modeled. Computational molecular biology brought a similar approach to the study of living systems. In both cases, hypotheses concerning the structure, function, and evolution of cognitive systems (natural as well as synthetic) take the form of computer programs that store, organize, manipulate, and use information. Systems whose information processing structures are fully programmed are difficult to design for all but the simplest applications. Real-world environments call for systems that are able to modify their behavior by changing their information processing structures. Cognitive and information structures and processes, embodied in living systems, display many effective designs for biological intelligent agents. They are also a source of ideas for designing artificial intelligent agents. This book explores a central issue in artificial intelligence, cognitive science, and artificial life: how to design information structures and processes that create and adapt intelligent agents through evolution and learning. The book is organized around four topics: the power of evolution to determine effective solutions to complex tasks, mechanisms to make evolutionary design scalable, the use of evolutionary search in conjunction with local learning algorithms, and the extension of evolutionary search in novel directions.
(source: Nielsen Book Data)

• Introduction to agent engineering, J.-M. Liu et al
• why autonomy makes the agent, S. Joseph and T. Kawamura
• knowledge granularity spectrum, action pyramid and the scaling problem, Y.-M. Ye and J.K. Tsotsos
• the motivation for dynamic decision-making frameworks in multi-agent systems, K.S. Barber and C.E. Martin
• dynamically organizing KDD processes in a multi-agent KDD system, N. Zhong et al
• self-organized intelligence, J.-M. Liu
• valuation-based coalition formation in multi-agent systems, S.J. Johansson
• simulating how to cooperate in iterated chicken and prisoner's dilemma games, B. Carlsson
• training intelligent agents using human data collected on the Internet, E. Sklar et al
• agent dynamics - soap paradigm, F.W.K. Lor.
• (source: Nielsen Book Data)

Agent engineering concerns the development of autonomous computational or physical entities capable of perceiving, reasoning, adapting, learning, cooperating and delegating in a dynamic environment. It is one of the most promising areas of research and development in information technology, computer science and engineering.This book addresses some of the key issues in agent engineering: What is meant by "autonomous agents"? How can we build agents with autonomy? What are the desirable capabilities of agents with respect to surviving (they will not die) and living (they will furthermore enjoy their being or existence)? How can agents cooperate among themselves? In order to achieve the optimal performance at the global level, how much optimization at the local, individual level and how much at the global level would be necessary?
(source: Nielsen Book Data)

• Introduction to agent engineering, J.-M. Liu et al
• why autonomy makes the agent, S. Joseph and T. Kawamura
• knowledge granularity spectrum, action pyramid and the scaling problem, Y.-M. Ye and J.K. Tsotsos
• the motivation for dynamic decision-making frameworks in multi-agent systems, K.S. Barber and C.E. Martin
• dynamically organizing KDD processes in a multi-agent KDD system, N. Zhong et al
• self-organized intelligence, J.-M. Liu
• valuation-based coalition formation in multi-agent systems, S.J. Johansson
• simulating how to cooperate in iterated chicken and prisoner's dilemma games, B. Carlsson
• training intelligent agents using human data collected on the Internet, E. Sklar et al
• agent dynamics - soap paradigm, F.W.K. Lor.
• (source: Nielsen Book Data)

Agent engineering concerns the development of autonomous computational or physical entities capable of perceiving, reasoning, adapting, learning, cooperating and delegating in a dynamic environment. It is one of the most promising areas of research and development in information technology, computer science and engineering.This book addresses some of the key issues in agent engineering: What is meant by "autonomous agents"? How can we build agents with autonomy? What are the desirable capabilities of agents with respect to surviving (they will not die) and living (they will furthermore enjoy their being or existence)? How can agents cooperate among themselves? In order to achieve the optimal performance at the global level, how much optimization at the local, individual level and how much at the global level would be necessary?
(source: Nielsen Book Data)

• Behavioural modelling, planning, and learning
• synthetic autonomy
• dynamics of distributed computation
• self-organized autonomy in multi-agent systems
• autonomy-oriented computation
• dynamics and complexity of autonomy-oriented computation.
• (source: Nielsen Book Data)

An autonomous agent is a computational system that acquires sensory data from its environment and decides by itself how to relate the external stimulus to its behaviors in order to attain certain goals. Responding to different stimuli received from its task environment, the agent may select and exhibit different behavioral patterns. The behavioral patterns may be carefully predefined or dynamically acquired by the agent based on some learning and adaptation mechanism(s). In order to achieve structural flexibility, reliability through redundancy, adaptability, and reconfigurability in real-world tasks, some researchers have started to address the issue of multiagent cooperation. Broadly speaking, the power of autonomous agents lies in their ability to deal with unpredictable, dynamically changing environments. Agent-based systems are becoming one of the most important computer technologies, holding out many promises for solving real-world problems.The aims of this book are to provide a guided tour to the pioneering work and the major technical issues in agent research, and to give an in-depth discussion on the computational mechanisms for behavioral engineering in autonomous agents. Through a systematic examination, the book attempts to provide the general design principles for building autonomous agents and the analytical tools for modeling the emerged behavioral properties of a multiagent system.
(source: Nielsen Book Data)

• Behavioural modelling, planning, and learning
• synthetic autonomy
• dynamics of distributed computation
• self-organized autonomy in multi-agent systems
• autonomy-oriented computation
• dynamics and complexity of autonomy-oriented computation.
• (source: Nielsen Book Data)

An autonomous agent is a computational system that acquires sensory data from its environment and decides by itself how to relate the external stimulus to its behaviors in order to attain certain goals. Responding to different stimuli received from its task environment, the agent may select and exhibit different behavioral patterns. The behavioral patterns may be carefully predefined or dynamically acquired by the agent based on some learning and adaptation mechanism(s). In order to achieve structural flexibility, reliability through redundancy, adaptability, and reconfigurability in real-world tasks, some researchers have started to address the issue of multiagent cooperation. Broadly speaking, the power of autonomous agents lies in their ability to deal with unpredictable, dynamically changing environments. Agent-based systems are becoming one of the most important computer technologies, holding out many promises for solving real-world problems.The aims of this book are to provide a guided tour to the pioneering work and the major technical issues in agent research, and to give an in-depth discussion on the computational mechanisms for behavioral engineering in autonomous agents. Through a systematic examination, the book attempts to provide the general design principles for building autonomous agents and the analytical tools for modeling the emerged behavioral properties of a multiagent system.
(source: Nielsen Book Data)

• Front Cover; Foundations of Genetic Algorithms6; Copyright Page; Contents;
• Chapter 1. Introduction;
• Chapter 2. Overcoming Fitness Barriers in Multi-Modal Search Spaces;
• Chapter 3. Niches in NK-Landscapes;
• Chapter 4. New Methods for Tunable, Random Landscapes;
• Chapter 5. Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem;
• Chapter 6. Direct Statistical Estimation of GA Landscape Properties;
• Chapter 7. Comparing Population Mean Curves;
• Chapter 8. Local Performance of the ((/(I, () -ES in a Noisy Environment

• Chapter 9. Recursive Conditional Scheme Theorem, Convergence and Population Sizing in Genetic Algorithms
• Chapter 10. Towards a Theory of Strong Overgeneral Classifiers;
• Chapter 11. Evolutionary Optimization through PAC Learning;
• Chapter 12. Continuous Dynamical System Models of Steady-State Genetic Algorithms;
• Chapter 13. Mutation-Selection Algorithm: A Large Deviation Approach;
• Chapter 14. The Equilibrium and Transient Behavior of Mutation and Recombination;
• Chapter 15. The Mixing Rate of Different Crossover Operators;
• Chapter 16. Dynamic Parameter Control in Simple Evolutionary Algorithms

• Chapter 17. Local Search and High Precision Gray Codes: Convergence Results and Neighborhoods
• Chapter 18. Burden and Benefits of Redundancy; Author Index; Key Word Index

Foundations of Genetic Algorithms, Volume 6 is the latest in a series of books that records the prestigious Foundations of Genetic Algorithms Workshops, sponsored and organised by the International Society of Genetic Algorithms specifically to address theoretical publications on genetic algorithms and classifier systems. Genetic algorithms are one of the more successful machine learning methods. Based on the metaphor of natural evolution, a genetic algorithm searches the available information in any given task and seeks the optimum solution by replacing weaker populations with stronger ones. Includes research from academia, government laboratories, and industry Contains high calibre papers which have been extensively reviewed Continues the tradition of presenting not only current theoretical work but also issues that could shape future research in the field Ideal for researchers in machine learning, specifically those involved with evolutionary computation.

• Part 1: Fascinating Evolution
• Part 2: Evolutionary Computation
• Part 3: If Darwin was a Programmer
• Part 4: Evolution of Developmental Programs.
• (source: Nielsen Book Data)

An essential capacity of intelligence is the ability to learn. An artificially intelligent system that could learn would not have to be programmed for every eventuality; it could adapt to its changing environment and conditions just as biological systems do. Illustrating Evolutionary Computation with Mathematica introduces evolutionary computation to the technically savvy reader who wishes to explore this fascinating and increasingly important field. Unique among books on evolutionary computation, the book also explores the application of evolution to developmental processes in nature, such as the growth processes in cells and plants. If you are a newcomer to the evolutionary computation field, an engineer, a programmer, or even a biologist wanting to learn how to model the evolution and coevolution of plants, this book will provide you with a visually rich and engaging account of this complex subject.
(source: Nielsen Book Data)

• Formal Agent Theoreis
• Computational Architecture and Infrastructure
• Learning and Adaptation
• Knowledge Discovery and Data Mining Agents
• Distributed Intelligence
• Agent Based Applications.
• (source: Nielsen Book Data)

This volume is an attempt to capture the essence of the state-of-the-art of intelligent agent technology and to identify the new challenges and opportunities that it is or will be facing. The most important feature of the volume is that it emphasizes a multi-faceted, holistic view of this emerging technology, from its computational foundations - in terms of models, methodologies, and tools for developing a variety of embodiments of agent-based systems - to its practical impact on tackling real-world problems.
(source: Nielsen Book Data)

• Introduction to Data Mining
• The Data Mining Process
• Data Mining Tools and Techniques
• Managing the Data Mining Project
• Modeling Data
• Deploying the Results
• The Discovery and Delivery of Knowledge for Effective Enterprise Outcomes: Knowledge Management
• Appendices: Glossary
• References
• Web Sites
• Data Mining and Knowledge Discovery Data Sets in the Public Domain
• Microsoft Solution Providers
• Summary of Knowledge Management Case Studies and Web Locations.
• (source: Nielsen Book Data)

Microsoft Data Mining approaches data mining from the particular perspective of IT professionals using Microsoft data management technologies. The author explains the new data mining capabilities in Microsoft's SQL Server 2000 database, Commerce Server, and other products, details the Microsoft OLE DB for Data Mining standard, and gives readers best practices for using all of them. The book bridges the previously specialized field of data mining with the new technologies and methods that are quickly making it an important mainstream tool for companies of all sizes. Data mining refers to a set of technologies and techniques by which IT professionals search large databases of information (such as those contained by SQL Server) for patterns and trends. Traditionally important in finance, telecommunication, and other information-intensive fields, data mining increasingly helps companies better understand and serve their customers by revealing buying patterns and related interests. It is becoming a foundation for e-commerce and knowledge management.
(source: Nielsen Book Data)

• Knowledge engineering and soft computing - an introduction, L.-Y. Ding. Part 1 Fuzzy knowledge-based systems: linguistic integrity - a framework for fuzzy modelling, AFRELI algorithm, J. Espinosa and J. Vandewalle
• a new approach to acquisition of comprehensible fuzzy rules, H. Ohno and T. Furuhashi
• fuzzy rule generation with fuzzy singleton-type reasoning method, Y. Shi and M. Mizumoto
• antecedent validity adaptation principle for table look-up scheme, P.-T. Chan and A.B. Rad
• fuzzy spline interpolation in sparse fuzzy rule bases, M.F. Kawaguchi and M. Miyakoshi
• revision principles applied for approximate reasoning, L.-Y. Ding et al
• handling null queries with compound fuzzy attributes, S.-L. Wang and Y.-J. Tsai
• fuzzy system description language, K. Otsuka et al. Part 2 Knowledge representation, integration and discovery by soft computing: knowledge representation and similarity measures in learning a vague legal concept, M.Q. Xu et al
• trend fuzzy sets and recurrent fuzzy rules for ordered dataset modelling, J.F. Baldwin et al
• approaches to the design of classification systems from numerical data and linguistic knowledge, H. Ishibuchi et al
• a clustering based on self-organizing map and knowledge discovery by neural network, K. Nakagawa et al
• probabilistic rough induction, J.-Z. Dong et al
• data mining via linguistic summaries of databases - an interactive approach, J. Kacprzyk and S. Zadrozny. (Part contents).
• (source: Nielsen Book Data)

Soft computing (SC) consists of several computing paradigms, including neural networks, fuzzy set theory, approximate reasoning, and derivative-free optimization methods such as genetic algorithms. The integration of those constituent methodologies forms the core of SC. In addition, the synergy allows SC to incorporate human knowledge effectively, deal with imprecision and uncertainty, and learn to adapt to unknown or changing environments for better performance. Together with other modern technologies, SC and its applications exert unprecedented influence on intelligent systems that mimic human intelligence in thinking, learning, reasoning, and many other aspects.Knowledge engineering (KE), which deals with knowledge acquisition, representation, validation, inferencing, explanation, and maintenance, has made significant progress recently, owing to the indefatigable efforts of researchers. Undoubtedly, the hot topics of data mining and knowledge/data discovery have injected new life into the classical AI world.This book tells readers how KE has been influenced and extended by SC and how SC will be helpful in pushing the frontier of KE further. It is intended for researchers and graduate students to use as a reference in the study of knowledge engineering and intelligent systems. The reader is expected to have a basic knowledge of fuzzy logic, neural networks, genetic algorithms, and knowledge-based systems.
(source: Nielsen Book Data)

• Knowledge engineering and soft computing - an introduction, L.-Y. Ding. Part 1 Fuzzy knowledge-based systems: linguistic integrity - a framework for fuzzy modelling, AFRELI algorithm, J. Espinosa and J. Vandewalle
• a new approach to acquisition of comprehensible fuzzy rules, H. Ohno and T. Furuhashi
• fuzzy rule generation with fuzzy singleton-type reasoning method, Y. Shi and M. Mizumoto
• antecedent validity adaptation principle for table look-up scheme, P.-T. Chan and A.B. Rad
• fuzzy spline interpolation in sparse fuzzy rule bases, M.F. Kawaguchi and M. Miyakoshi
• revision principles applied for approximate reasoning, L.-Y. Ding et al
• handling null queries with compound fuzzy attributes, S.-L. Wang and Y.-J. Tsai
• fuzzy system description language, K. Otsuka et al. Part 2 Knowledge representation, integration and discovery by soft computing: knowledge representation and similarity measures in learning a vague legal concept, M.Q. Xu et al
• trend fuzzy sets and recurrent fuzzy rules for ordered dataset modelling, J.F. Baldwin et al
• approaches to the design of classification systems from numerical data and linguistic knowledge, H. Ishibuchi et al
• a clustering based on self-organizing map and knowledge discovery by neural network, K. Nakagawa et al
• probabilistic rough induction, J.-Z. Dong et al
• data mining via linguistic summaries of databases - an interactive approach, J. Kacprzyk and S. Zadrozny. (Part contents).
• (source: Nielsen Book Data)

Soft computing (SC) consists of several computing paradigms, including neural networks, fuzzy set theory, approximate reasoning, and derivative-free optimization methods such as genetic algorithms. The integration of those constituent methodologies forms the core of SC. In addition, the synergy allows SC to incorporate human knowledge effectively, deal with imprecision and uncertainty, and learn to adapt to unknown or changing environments for better performance. Together with other modern technologies, SC and its applications exert unprecedented influence on intelligent systems that mimic human intelligence in thinking, learning, reasoning, and many other aspects.Knowledge engineering (KE), which deals with knowledge acquisition, representation, validation, inferencing, explanation, and maintenance, has made significant progress recently, owing to the indefatigable efforts of researchers. Undoubtedly, the hot topics of data mining and knowledge/data discovery have injected new life into the classical AI world.This book tells readers how KE has been influenced and extended by SC and how SC will be helpful in pushing the frontier of KE further. It is intended for researchers and graduate students to use as a reference in the study of knowledge engineering and intelligent systems. The reader is expected to have a basic knowledge of fuzzy logic, neural networks, genetic algorithms, and knowledge-based systems.
(source: Nielsen Book Data)

• Automatic detection of microcalcifications in mammograms using a fuzzy classifier, A.P. Drijarkara et al
• predictive fuzzy model for control of an artificial muscle, P.B. Petrovi
• evolutionary computation for information retrieval based on user preference, H.-G. Kim and S.-B. Cho
• fuzzy logic and neural networks approach - a way to improve overall performance of integrated heating systems, E. Entchev
• design and tuning a neurofuzzy power system stabilizer using genetic algorithms, A. Afzalian and D.A. Linkens
• an application of logic programmes with soft computing aspects to fault diagnosis in digital circuits, H. Sakai et al
• determination of the motion parameters from the perspective projection of a triangle, M.M. Sein and H. Hama. (Part contents).
• (source: Nielsen Book Data)

Soft computing has been presented not only with the theoretical developments but also with a large variety of realistic applications to consumer products and industrial systems. Application of soft computing has provided the opportunity to integrate human-like vagueness and real-life uncertainty into an otherwise hard computer program.This book highlights some of the recent developments in practical applications of soft computing in engineering problems. All the chapters have been sophisticatedly designed and revised by international experts to achieve wide but in-depth coverage.
(source: Nielsen Book Data)

• Getting started
• Perceptron learning -- basics
• Choice of learning rules
• Augmented statistical mechanics formulation
• Noisy teachers
• Storage problem
• Discontinuous learning
• Unsupervised learning
• On-line learning
• Making contact with statistics
• Bird's eye view: multifractals
• Multilayer networks
• On-line learning in multilayer networks
• What else?

Learning is one of the things that humans do naturally, and it has always been a challenge for us to understand the process. Nowadays this challenge has another dimension as we try to build machines that are able to learn and to undertake tasks such as datamining, image processing and pattern recognition. We can formulate a simple framework, artificial neural networks, in which learning from examples may be described and understood. The contribution to this subject made over the last decade by researchers applying the techniques of statistical mechanics is the subject of this book. The authors provide a coherent account of various important concepts and techniques that are currently only found scattered in papers, supplement this with background material in mathematics and physics and include many examples and exercises to make a book that can be used with courses, or for self-teaching, or as a handy reference.
(source: Nielsen Book Data)

• 1. Introduction
• 2. The strategic-negotiation model
• 3. Negotiations about data allocation
• 4. Negotiations about resource allocation
• 5. Negotiations about resource allocation with multiple attributes
• 6. Negotiations about task distribution
• 7. Negotiations about how to reduce pollution
• 8. Negotiation during a hostage crisis
• 9. Economic and game-theoretic models for cooperation
• 10. Conclusions and future directions.

A model for strategic negotiation for intelligent agents. As computers advance from isolated workstations to linked elements in complex communities of systems and people, cooperation and coordination via intelligent agents become increasingly important. Examples of such communities include the Internet, electronic commerce, health institutions, electricity networks, and digital libraries. Sarit Kraus is concerned here with the cooperation and coordination of intelligent agents that are self-interested and usually owned by different individuals or organizations. Conflicts frequently arise, and negotiation is one of the main mechanisms for reaching agreement. Kraus presents a strategic-negotiation model that enables autonomous agents to reach mutually beneficial agreements efficiently in complex environments. The model, which integrates game theory, economic techniques, and heuristic methods of artificial intelligence, can be automated in computer systems or applied to human situations. The book provides both theoretical and experimental results.
(source: Nielsen Book Data)

The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run "programs"; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior-thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI, " and "behavior-based AI." This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building. The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.
(source: Nielsen Book Data)

• Consideration of emotion model and primitive language of robots, T. Ogata and S. Sugano
• an architecture for animal-like behaviour selection, T. Kitamura
• a computation literary theory -the ultimate products of the brain/mind machine, A. Tokosumi
• cooperation between neural networks within the brain, M. Dufosse et al
• brain-like functions in evolving connectionist systems for on-line, knowledge-based learning, N. Kasabov
• interrelationships, communication, semiotics and artificial consciousness, H.-N.L. Teodorescu
• time emerges from incomplete clock, based on internal measurement, Y.-P. Gunji et al
• the logical jump in shell changing in hermit crab and tool experiment in ants, M. Kitabayashi et al
• the neurobiology of semantics - how can machines be designed to have meanings, W.J. Freeman
• the emergence of contentful experience, M.H. Bickhard
• intentionality and foundations of logic - a new approach to neurocomputation, G. Basti.
• (source: Nielsen Book Data)

This volume is a guide to two types of transcendence of academic borders which seem necessary for understanding and modelling brain function. The first type is technical transcendence needed to make intelligent machines such as a humanoid robot, an animal-like behavior architecture, an interpreter of fiction, and an evolving learning machine. This technical erosion is conducted into areas such as biology, ethology, neuroscience and psychology, as well as robotics and soft computing. The second type of transcendence of cross-disciplinary boundaries cuts across scientific areas such as biology and cognitive science/philosophy, into comprehensive, less technical and more abstract aspects of brain function. These aspects enable us to know in what direction and how far an intelligent machine will go.
(source: Nielsen Book Data)

• Consideration of emotion model and primitive language of robots, T. Ogata and S. Sugano
• an architecture for animal-like behaviour selection, T. Kitamura
• a computation literary theory -the ultimate products of the brain/mind machine, A. Tokosumi
• cooperation between neural networks within the brain, M. Dufosse et al
• brain-like functions in evolving connectionist systems for on-line, knowledge-based learning, N. Kasabov
• interrelationships, communication, semiotics and artificial consciousness, H.-N.L. Teodorescu
• time emerges from incomplete clock, based on internal measurement, Y.-P. Gunji et al
• the logical jump in shell changing in hermit crab and tool experiment in ants, M. Kitabayashi et al
• the neurobiology of semantics - how can machines be designed to have meanings, W.J. Freeman
• the emergence of contentful experience, M.H. Bickhard
• intentionality and foundations of logic - a new approach to neurocomputation, G. Basti.
• (source: Nielsen Book Data)

This volume is a guide to two types of transcendence of academic borders which seem necessary for understanding and modelling brain function. The first type is technical transcendence needed to make intelligent machines such as a humanoid robot, an animal-like behavior architecture, an interpreter of fiction, and an evolving learning machine. This technical erosion is conducted into areas such as biology, ethology, neuroscience and psychology, as well as robotics and soft computing. The second type of transcendence of cross-disciplinary boundaries cuts across scientific areas such as biology and cognitive science/philosophy, into comprehensive, less technical and more abstract aspects of brain function. These aspects enable us to know in what direction and how far an intelligent machine will go.
(source: Nielsen Book Data)

• Cover; Title page; Preface; Acknowledgments; Contents; I. Data Collection; Toward Active Mining from On-line Scientific Text Abstracts Using Pre-existing Sources; Data Mining on the WAVEs
• Word-of-mouth-Assisting Virtual Environments; Immune Network-based Clustering for WWW Information Gathering/Visualization; Interactive Web Page Retrieval with Relational Learning-based Filtering Rules; Monitoring Partial Update of Web Pages by Interactive Relational Learning; Context-based Classification of Technical Terms Using Support Vector Machines.

Focusing on data mining, this work is a joint effort from researchers in Japan, and includes a report on the forefront of data collection, user-centred mining and user interaction/reaction. It offers an overview of modern solutions with real-world applications, sharing hard-learned experiences.

• Cover; Title page; Contents; Retriever Prototype of a Case Based Reasoning: A Study Case; Dynamic Compaction Process of Metal Powder Media within Dies; Automated Theorem Proving for Many-sorted Free Description Theory Based on Logic Translation; Annotated Logic and Negation as Failure; Multi-agent System for Distribution System Operation; ArTbitrariness: Putting Computer Creativity to Work in Aesthetic Domains; An Overview of Fuzzy Numbers and Fuzzy Arithmetic; The Brain and Arithmetic Calculation; Evolving Arithmetical Knowledge in a Distributed Intelligent Processing System.

Logic (both classical and non-classical) is being increasingly related with other fields in almost every scientific discipline and human activity. This work covers its role in artificial intelligence, robotics, informatics, technology, and correlated themes.

The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2001 conference.
(source: Nielsen Book Data)

• Part 1 - Setting the Stage
• Chapter 1 - Intelligent Machines: Imitating Life
• Chapter 2 - Deep Blue: A Triumph of AI?
• Chapter 3 - Building An Artificial Brain
• Chapter 4 - Evolutionary Computation: Putting Nature to Work
• Chapter 5 - Blue Hawaii: Natural Selection
• Chapter 6 - Checkers
• Chapter 7 - Chinook: The Man-machine Checkers Champion
• Chapter 8 - Samuel's Learning Machine
• Chapter 9 - The Samuel-Newell Challenge
• Part 2 - The Making of Blondie
• Chapter 10 - Evolving in the Checkers Environment
• Chapter 11 - In The Zone
• Chapter 12 - A Repeat Performance
• Chapter 13 - A New Dimension
• Chapter 14 - Letting the Genie Out of the Bottle
• Chapter 15 - Blondie24 Epilogue: The Future of Artificial Intelligence Appendix: Your Honor, I Object! Notes Index About the Author.
• (source: Nielsen Book Data)

Blondie24 tells the story of a computer that taught itself to play checkers far better than its creators ever could by using a program that emulated the basic principles of Darwinian evolution--random variation and natural selection-- to discover on its own how to excel at the game. Unlike Deep Blue, the celebrated chess machine that beat Garry Kasparov, the former world champion chess player, this evolutionary program didn't have access to strategies employed by human grand masters, or to databases of moves for the endgame moves, or to other human expertise about the game of chekers. With only the most rudimentary information programmed into its "brain, " Blondie24 (the program's Internet username) created its own means of evaluating the complex, changing patterns of pieces that make up a checkers game by evolving artificial neural networks---mathematical models that loosely describe how a brain works. It's fitting that Blondie24 should appear in 2001, the year when we remember Arthur C. Clarke's prediction that one day we would succeed in creating a thinking machine. In this compelling narrative, David Fogel, author and co-creator of Blondie24, describes in convincing detail how evolutionary computation may help to bring us closer to Clarke's vision of HAL. Along the way, he gives readers an inside look into the fascinating history of AI and poses provocative questions about its future.
(source: Nielsen Book Data)

• FOREWORD; CONTENTS; Keynote Address; The Origin of Complexity (presented, but paper not submitted); Plenary Session; The Role of Field Coupling in Nano-Scale Cellular Nonlinear Networks; Session Theory I; On the Relationship Between CNNs and PDEs; New Spatial-Temporal Patterns and the First Programmable On-Chip Bifurcation Test-Bed; On Stability of Full Range and Polynomial Type CNNs; A Study on Limit Cycles in Nearly Symmetric Cellular Neural Networks; An Improved Global Stability Result for Cellular Neural Networks with Time Delay; Session Applications I.

• SC-CNNs for Chaotic Signals GenerationMoving Object Tracking on Panoramic Images; MPEG-4 Based Modifications for an CNN Segmentation Chip; Watermarking for the Authentication of Video on CNN-UM; An Analogic CNN-Algorithm of Pixel Level Snakes for Tracking and Surveillance Tasks; Session Theory II; On the Dynamics of a Class of Cellular Neural Networks; mL-CNN: A CNN Model for Reaction-Diffusion Processes in m-Component Systems; Emergence of Global Patterns in Connected Neural Networks; Influence of System Non-Uniformity on Dynamic Phenomena in Arrays of Coupled Nonlinear Networks.

• Boolean Design of Binary Initialized and Coupled CNN Image Processing OperatorsSession Physical Implementations I; ACE16K: A 128x128 Focal Plane Analog Processor with Digital I/O.; On the RTD Implementation of Simplicial Cellular Nonlinear Networks; Programmable Optical CNN Implementation Based on the Template Pixels' Angular Coding; Application Issues Of A Programmable Optical CNN Implementation; Configurable Multi-Layer CNN-UM Emulator on FPGA; Session Applications II (Poster); Object-Oriented Image Analysis via Analogic CNN Algorithms
• Part I: Motion Estimation.

• Object-Oriented Image Analysis via Analogic CNN Algorithms
• Part II: Image Synthesis and Consistency ObservationA CNN Path Planning for a Mobile Robot in an Environment with Obstacles; A CNN Based System to Blind Sources Separation of MEG Signals; Delay-Driven Contrast Enhancement Using a Cellular Neural Network with State-Dependant Delay; Plenary Session; Time as Coding Space for Information Processing in the Cerebral Cortex; Characterizing the Spatio-Temporal Dynamics of the Epileptogenic Process with Nonlinear EEG Analyses; Session Bionics and Biologically Relevant Models I.

• Analyzing Multidimensional Neural Activity via CNN-UMBasic Mammalian Retinal Effects on the Prototype Complex Cell CNN Universal Machine; Prediction of Epileptic Seizures by CNN with Linear Weight Functions; Biometric Authentication Based on Perceptual Resonance between CNN Emergent Patterns and Humans; CNN Based Central Pattern Generators with Sensory Feedback; Session Integrated Sensing and Processing; Mobile Sensor-Actuator Networks: Opportunities and Challenges; Visual Feedback by Using a CNN Chip Prototype System; Multi-Target Tracking with Stored Program Adaptive CNN Universal Machines.

This volume covers the fundamental theory of Cellular Neural Networks as well as their applications in various fields such as science and technology. It contains all 83 papers of the 7th International Workshop on Cellular Neural Networks and their Applications. The workshop follows a biennial series of six workshops consecutively hosted in Budapest (1990), Munich, Rome, Seville, London and Catania (2000).

• Invited lectures. Trends in computational intelligence in nuclear engineering / R.E. Uhrig
• Possibility theory in decision support systems / H. Prade
• Computing with words-semantics / P.P. Wang
• pt. 1. Recent developments. Computing with words: the concept of conformity / M. Gemeinder
• Semantic additive and non additive measure as modal logic model of fuzzy sets and fuzzy measure / G. Resconi and I.B. Türksen
• A new lattice-valued propositional logic (I): semantics / K. Qin and Y. Xu
• Fuzzy numerical methods / I. Perfilieva
• Solution of Dirichlet boundary value problem for the Poisson equation based on a fuzzy system / B.S. Moon [and others]
• A strong relevant logic approach to the calculus of fuzzy conditionals / J. Cheng and Y. Goto
• The [symbol]-resolution field of indecomposable extremely simple form of lattice-valued propositional logic LP(X) / W. Wang [and others]
• Fuzzy relation equations via basic predicate fuzzy logic / V. Novák, I. Perfilieva and S. Gottwald
• Underlying criteria in valued preference relations / J. Montero [and others]
• Model predictive control using fuzzy satisfactory optimization / S. Li and W. Qu
• Mirror strategy in game playing: combining fuzzy sets and genetic algorithm to improve strategies / A. Cincotti, V. Cutello and G. Sorace
• The extraction of linguistic knowledge using fuzzy logic and generalized quantifiers / A. Dvorák and V. Novák
• A speed improvement scheme in knowledge discovery of association rule algorithm for a market basket / W. Premchaiswadi [and others]
• Dynamic clustering based on maximum frequent itemsets for analysing purchase pattern / Y. Zhao and P. Shi
• Using soft computing techniques to integrate multiple kinds of attributes in data mining / S. Coppock and L. Mazlack
• Discovering association rules with degrees of support and implication (ARsi) / G. Chen [and others]
• The role of Occam's razor in activity based modeling / E. Moons [and others]
• Reliability demonstration of interior-outer-set mode relevant in risk assessment / C. Huang
• Flexibility quantification in computer integrated manufacturing systems based on fuzzy cash flow analysis / A. Beskese, C. Kahraman and D. Ruan
• About the fusion of technical-specification knowledge components / E. Grégoire
• Information diffusion method in risk analysis / H. Strang [and others]
• The theory of optimal information diffusion estimation and its application / X. Wang and Y. You
• Risk factor analysis and evaluation of natural disasters: application of the RIFAE framework to the 2000 TokaiFlood Disaster in Japan / G. Zhai [and others]
• Towards a soft representation of diversity in reliability theory / M. Oussalah and L. Strigini
• Analogy between multistate system and nonadditive reliability models / M. Oussalah and M. Newby.

• pt. 2. Computational intelligent systems. An architecture model of intelligent agents system to approach problems of distributed knowledge / F. Llorens, R. Rizo and M. Pujol
• Design of a time-delay feedback controller to chaotify continuous-time Takagi-Sugeno fuzzy systems / Z. Li and B. Zhang
• On-line inference for fuzzy controllers in continuous domains / S. Lu
• Generalized predictive control with fuzzy constraints simulation / S. Li, G. Du and W. Qu
• Process identification through modular neural networks and rule extraction / B.J. van der Zwaag, K. Slump and L. Spaanenburg
• Automated image enhancement algorithm based on optimization of generalized fuzzy entropies (GFEs) / I.K. Vlachos and G.D. Sergiadis
• Integration of human knowledge and measured data for optimization of fabric hand / X. Zeng [and others]
• Fuzzy hidden Markov models for online training evaluation in virtual reality simulators / R.M. de Moraes and L. dos Santos Machado
• A new approach for plant monitoring using type-2 fuzzy logic and fractal theory / O. Castillo and P. Melin
• Integration of fuzzy techniques and perception systems for ITS / R. Garcia [and others]
• Digital neural networks in the CAM-brain machine / H. Eeckhaut and J. Van Campenhout
• Hysteresis modelling using feed-forward neural networks and its application to soft magnetic materials / D. Makaveev, L. Dupré and J. Melkebeek
• A new method for adaptive control of non-linear plants using type-2 fuzzy logic and neural networks / P. Melin and O. Castillo
• Analysis and modeling of field device tool interfaces / W. Zhang and C. Diedrich
• Comparing a genetic fuzzy and a neurofuzzy classifier for credit scoring / F. Hoffmann [and others]
• A modular neural network classifier for the recognition of occluded characters in automatic license plate reading / J.A.G. Nijhuis, A. Broersma and L. Spaanenburg
• Noise cleaning process using the fuzzy membership function / A. Thammano and P. Ruxpakawong
• A hybrid scheme, ART1 and feature matching, for Thai character recognition systems / N. Premchaiswadi [and others]
• Environmental systems modelling and diagnosis using a multiple model approach / K. Gasso, G. Mourot and J. Ragot
• Risk assessment of avalanches
• a fuzzy GIS application / M. Hemetsberger [and others]
• Land evaluation for surface irrigation using fuzzy representation and aggregation schemes / A. Tocatlidou [and others]
• Annoyance prediction with fuzzy rule bases / A. Verkeyn and D. Botteldooren.

• pt. 3. Applied research and nuclear applications. Real-time intelligent vision sensor for robot navigation using symmetry features / D. Popescu, K. Huebner and J. Zhang
• Self-organizing fuzzy system for vision-based position estimation / N. Popescu and J. Zhang
• Fuzzy adaptive robust tracking control for a class of uncertain nonlinear systems and its application / Y. Yang and C. Zhou
• Motion control of an hexapod walking machine using fuzzy state automata / D. Morano and L.M. Reyneri
• Equivalent linearization of 2-way fuzzy adaptive system under nonparametric uncertainty and inconsistency / E. Gurkan [and others]
• Adaptive fuzzy neural control of nonlinear systems / Y. Gao, M.J. Er and C. Deng
• Toward perception-based robotics: a fuzzy-arithmetic-based Lyapunov synthesis approach / C. Zhou, P.K. Yue and H.L. Sng
• Algorithmic lateral inhibition as a generic method for visual information processing with potential applications in robotics / A.E. Delgado and J. Mira
• A reinforcement learning method for dynamic obstacle avoidance in robotic mechanisms / D. Maravall and J. de Lope
• Intelligent navigation in partially unknown indoor environments / A. Poncela [and others]
• Evolutionary active force control of a 5-link biped robot / L.C. Kwek [and others]
• Synthesis and evaluation analysis of the physical model indicator information by computing with words / J. Liu, D. Ruan and R. Carchon
• Fuzzy-logic supported evaluation of the disposal costs and tariffs of high-level radioactive waste / P.L. Kunsch, A. Fiordaliso and P. Fortemps
• Designing reduced scale thermal-hydraulic experiments using genetic algorithms / C.M.F. Lapa, P.A.B. de Sampaio and C.M.N.A. Pereira
• Genetic based transient identification system design with automatic selection of meaningful variables / C.M.N.A. Pereira and R. Schirru
• Optimization of fuel reload in a BWR nuclear reactor using a recurrent neural network / J.J. Ortiz and I. Requena
• Neural network methods for radial profile reconstruction for ZEFF from bremsstrahlung data on the Textor tokamak / G. Verdoolaege, G. Telesca and G. Van Oost
• The use of non linear partial least square methods for on-line process monitoring as an alternative to artificial neural networks / P.F. Fantoni [and others]
• Improving feedwater cross-correlation flow measurements in nuclear power plants with artifical neural networks / D. Roverso, D. Ruan and P.F. Fantoni
• Application of localized regularization methods for nuclear power plant sensor calibration monitoring / M.A. Buckner [and others].

FLINS is an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science. FLINS 2002 is the fifth in a series of FLINS conferences and covers state-of-the-art research and development in computational intelligence for applied research in general and for nuclear science and engineering in particular. This book outlines the trends in computational intelligence in control, decision-making, and nuclear engineering, and presents the latest developments of computational intelligent systems in applied research and nuclear applications.
(source: Nielsen Book Data)

• 1. The vision of sociable robots
• 2. Robot in society: a question of interface
• 3. Insights from developmental psychology
• 4. Designing sociable robots
• 5. The physical robot
• 6. The vision system
• 7. The auditory system
• 8. The motivation system
• 9. The behavior system
• 10. Facial animation and expression
• 11. Expressive vocalization system
• 12. Social constraints on animate vision
• 13. Grand challenges of building sociable robots.

Cynthia Breazeal here presents her vision of the sociable robot of the future, a synthetic creature and not merely a sophisticated tool. A sociable robot will be able to understand us, to communicate and interact with us, to learn from us and grow with us. It will be socially intelligent in a humanlike way. Eventually sociable robots will assist us in our daily lives, as collaborators and companions. Because the most successful sociable robots will share our social characteristics, the effort to make sociable robots is also a means for exploring human social intelligence and even what it means to be human. Breazeal defines the key components of social intelligence for these machines and offers a framework and set of design issues for their realization. Much of the book focuses on a nascent sociable robot she designed named Kismet. Breazeal offers a concrete implementation for Kismet, incorporating insights from the scientific study of animals and people, as well as from artistic disciplines such as classical animation. This blending of science, engineering, and art creates a lifelike quality that encourages people to treat Kismet as a social creature rather than just a machine. The book includes a CD-ROM that shows Kismet in action.
(source: Nielsen Book Data)

Annotation Intelligent Technologies including neural network, evolutionary computations, fuzzy approach and mainly hybrid approaches are very promising tools to build intelligent technologies in general. The progress of each theory or application is provided by a number of various theoretical as well as applicational experiments. Machine intelligence is the only alternative how to increase the level of technology to make technology more human-centred and more effective for society. This book includes theoretical as well as applicational papers in the field of neural networks, fuzzy systems and mainly evolutionary computations which application potential was increased by enormous progress in computer power. Hybrid technologies are still progressing and are trying to make some more applications with their ability to learn and process fuzzy information. Neurogenetic systems are very interesting approach to make systems re-configurable and on-line systems for real-world applications. The book is presenting papers from Japan, USA, Hungary, Poland, Germany, Finland, France, Slovakia, United Kingdom, Czech Republic and some other countries. This publication provides the latest state of the art in the field and could be contributed to theory and applications in the machine intelligence tools and their wide application potential in current and future technologies within the Information Society.

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier-a limited, but well-established and comprehensively studied model-and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
(source: Nielsen Book Data)

• Series Foreword; Preface; 1
• A Tutorial Introduction; I
• Concepts and Tools; 2
• Kernels; 3
• Risk and Loss Functions; 4
• Regularization; 5
• Elements of Statistical Learning Theory; 6
• Optimization; II
• Support Vector Machines; 7
• Pattern Recognition; 8
• Single-Class Problems: Quantile Estimation and Novelty Detection; 9
• Regression Estimation; 10
• Implementation; 11
• Incorporating Invariances; 12
• Learning Theory Revisited; III
• Kernel Methods; 13
• Designing Kernels; 14
• Kernel Feature Extraction; 15
• Kernel Fisher Discriminant; 16
• Bayesian Kernel Methods.

• 17
• Regularized Principal Manifolds18
• Pre-Images and Reduced Set Methods; A
• Addenda; B
• Mathematical Prerequisites; References; Index; Notation and Symbols.

A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs--kernels-for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
(source: Nielsen Book Data)

Annotation. This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing spareness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nystrom sampling with active selection of support vectors. The methods are illustrated with several examples.

Introducing Microsoft's flagship wireless development tool The .NET Mobile Web Developer's Guide will provide readers with a solid guide to developing mobile applications using Microsoft technologies. The focus of this book is on using ASP.NET and the .NET mobile SDK. It provides an introduction to the .NET platform and goes into moderate details on ASP.NET to allow readers to start developing ASP.NET applications. In addition, this book will give the readers the insight to use the various Microsoft technologies for developing mobile applications. This book assumes the readers have experience in developing web applications and are familiar with any one of the server-side technologies like ASP, JSP or PHP.
(source: Nielsen Book Data)