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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)