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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)