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 Stanford, California : HeurisTech Press, c1981.
 Description
 Book — 1 online resource (424 pages)
 Stanford, California : HeurisTech Press, c1982.
 Description
 Book — 1 online resource (443 pages)
 Stanford, California : HeurisTech Press, c1982.
 Description
 Book — 1 online resource (659 pages)
4. Evolution, learning, and cognition [1988]
 Singapore ; Teaneck, N.J., USA : World Scientific, ©1988.
 Description
 Book — 1 online resource (x, 411 pages) : illustrations
 Summary

 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; BACKPROPAGATION ALGORITHM FOR SUPERVISED LEARNING; Extended BackPropagation; 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 McCULLOCHPITTS; 2.1 StateTheoretic Description; 2.2 Associative Memory; 3 THE OUTERPRODUCT ALGORITHM; 3.1 The Model; 3.2 Storage Capacity; 4 SPECTRAL ALGORITHMS; 4.1 OuterProducts Revisited; 4.2 Constructive Spectral Approaches; 4.3 Basins of Attraction; 4.4 Choice of Eigenvalues; 5 COMPUTER SIMULATIONS; 6 DISCUSSION; A PROPOSITIONS.
 B OUTERPRODUCT 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 nexpansion of arbitrary l.s. functions; 2
 .4. Continuous versus discontinuous behaviour: transitions;
 3. General Boolean NE; 3
 .1. Linearization in tensor space; 3
 .2. Nextstate 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 HyperplaneDirected 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 2NEURON NETWORKS.
(source: Nielsen Book Data)
5. How to build a person : a prolegomenon [1989]
 Pollock, John L.
 Cambridge, Mass. : MIT Press, ©1989.
 Description
 Book — 1 online resource (xi, 189 pages) : illustrations
 Summary

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)
 Singapore ; Teaneck, N.J. : World Scientific, ©1990.
 Description
 Book — 1 online resource (vi, 222 pages) : illustrations
 Summary

 An intelligent imagebased computeraided education system: the prototype BIRDS / A.A. David, O. Thiery & M. Crehange
 PLAYMAKER: a knowledgebased 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 variablestroke internalcombustion 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 objectoriented 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 modelbased knowledge acquisition / W. Dilger & J. Moller
 PRIOPS: a realtime production system architecture for programming and learning in embedded systems / D.E. Parson & G.D. Blank.
(source: Nielsen Book Data)
7. Naturally intelligent systems [1990]
 Caudill, Maureen.
 Cambridge, Mass. : MIT Press, ©1990.
 Description
 Book — 1 online resource (304 pages) : illustrations
 Summary

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 downtoearth 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 downtoearth 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)
 Judd, J. Stephen.
 Cambridge, Mass. : MIT Press, ©1990.
 Description
 Book — 1 online resource (150 pages) : illustrations
 Summary

 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.
(source: Nielsen Book Data)
 Neurale netværk. English
 Brunak, Søren.
 Singapore ; Teaneck, N.J., USA : World Scientific, ©1990.
 Description
 Book — 1 online resource (180 pages) : illustrations
 Summary

Both specialists and laymen will enjoy reading this book. Using a lively, nontechnical 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)
10. Applications of learning & planning methods [1991]
 Singapore ; Teaneck, N.J. : World Scientific, 1991.
 Description
 Book — 1 online resource
 Summary

 Ch 1. Embedding learning in a general framebased 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 explanationbased 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. CalistriYeh
 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. Knowledgebased acquisition in realtime path planning in unknown space / N.G. Bourbakis
 ch. 14. Path planning for two cooperating robot manipulators / Q. Xue and P.C.Y. Sheu.
(source: Nielsen Book Data)
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