Books in Artificial intelligence general

91-100 of 102 results in All results

Foundational Issues in Artificial Intelligence and Cognitive Science

• 1st Edition
• Volume 109
• March 1, 1995
• Mark H. Bickhard + 1 more
• English
• eBook 978-0-08-086763-2

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The book focuses on a conceptual flaw in contemporary artificial intelligence and cognitive science. Many people have discovered diverse manifestations and facets of this flaw, but the central conceptual impasse is at best only partially perceived. Its consequences, nevertheless, visit themselves asdistortions and failures of multiple research projects - and make impossible the ultimate aspirations of the fields.The impasse concerns a presupposition concerning the nature of representation - that all representation has the nature of encodings: encodingism. Encodings certainly exist, butencodingism is at root logically incoherent; any programmatic research predicted on it is doomed too distortion and ultimate failure.The impasse and its consequences - and steps away from that impasse - are explored in a large number of projects and approaches. These include SOAR, CYC, PDP, situated cognition, subsumption architecture robotics, and the frame problems - a general survey of the current research in AI and Cognitive Science emerges.Interactivism, an alternative model of representation, is proposed and examined.

Foundations of Genetic Algorithms 1995 (FOGA 3)

• 1st Edition
• Volume 3
• March 7, 1994
• FOGA
• English
• eBook 978-1-4832-9502-2

  9 7 8 - 1 - 4 8 3 2 - 9 5 0 2 - 2

Foundations of Genetic Algorithms, 3 focuses on the principles, methodologies, and approaches involved in the integration of genetic algorithm into mainstream mathematics, as well as genetic operators, genetic programming, and evolutionary algorithms. The selection first offers information on an experimental design perspective on genetic algorithms; schema theorem and price's theorem; and fitness variance of formae and performance prediction. Discussions focus on representation-independent recombination, representation-independent mutation and hill-climbing, recombination and the re-emergence of schemata, and Walsh transforms and deception. The publication then examines the troubling aspects of a building block hypothesis for genetic programming and order statistics for convergence velocity analysis of simplified evolutionary algorithms. The manuscript ponders on stability of vertex fixed points and applications; predictive models using fitness distributions of genetic operators; and modeling simple genetic algorithms for permutation problems. Topics include exact models for permutations, fitness distributions of genetic operators, predictive model based on linear fitness distributions, and stability in the simplex. The book also takes a look at the role of development in genetic algorithms and productive recombination and propagating and preserving schemata. The selection is a dependable source of data for mathematicians and researchers interested in genetic algorithms.

Uncertainty in Artificial Intelligence

• 1st Edition
• November 5, 1993
• David Heckerman + 1 more
• English
• eBook 978-1-4832-1451-1

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Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.

Knowledge Processing and Applied Artificial Intelligence

• 1st Edition
• September 17, 1993
• Soumitra Dutta
• English
• eBook 978-1-4831-8392-3

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Knowledge Processing and Applied Artificial Intelligence discusses the business potential of knowledge processing and examines the aspects of applied artificial intelligence technology. The book is comprised of nine chapters that are organized into five parts. The text first covers knowledge processing and applied artificial intelligence, and then proceeds to tackling the techniques for acquiring, representing, and reasoning with knowledge. The next part deals with the process of creating and implementing strategically advantageous knowledge-based system applications. The fourth part covers intelligent interfaces, while the last part details alternative approaches to knowledge processing. The book will be of great use to students and professionals of computer or business related disciplines.

Decision Support Systems: Experiences and Expectations

• 1st Edition
• Volume 9
• July 6, 1992
• T. Jelassi + 2 more
• English
• eBook 978-1-4832-9843-6

  9 7 8 - 1 - 4 8 3 2 - 9 8 4 3 - 6

This proceedings volume aims to consolidate current knowledge of research into the many fields of DSS, and to identify key issues which should be incorporated into the future research agenda. The main themes of this volume include: DSS for distributed decision processes, Embedding knowledge in DSS, and DSS and organizational change.

Uncertainty in Artificial Intelligence

• 1st Edition
• July 1, 1992
• Didier J. Dubois + 2 more
• English
• eBook 978-1-4832-8287-9

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Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference (1992) covers the papers presented at the Eighth Conference on Uncertainty in Artificial Intelligence, held at Stanford University on July 17-19, 1992. The book focuses on the processes, methodologies, technologies, and approaches involved in artificial intelligence. The selection first offers information on Relative Evidential Support (RES), modal logics for qualitative possibility and beliefs, and optimizing causal orderings for generating DAGs from data. Discussions focus on reversal, swap, and unclique operators, modal representation of possibility, and beliefs and conditionals. The text then examines structural controllability and observability in influence diagrams, lattice-based graded logic, and dynamic network models for forecasting. The manuscript takes a look at reformulating inference problems through selective conditioning, entropy and belief networks, parallelizing probabilistic inference, and a symbolic approach to reasoning with linguistic quantifiers. The text also ponders on sidestepping the triangulation problem in Bayesian net computations; exploring localization in Bayesian networks for large expert systems; and expressing relational and temporal knowledge in visual probabilistic networks. The selection is a valuable reference for researchers interested in artificial intelligence.

Paradigms of Artificial Intelligence Programming

• 1st Edition
• October 1, 1991
• Peter Norvig
• English
• Paperback 978-1-55860-191-8

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• eBook 978-0-08-057115-7

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Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems. By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions. This volume is an excellent text for a course on AI programming, a useful supplement for general AI courses and an indispensable reference for the professional programmer.

COLT '91

• 1st Edition
• July 1, 1991
• COLT
• English
• eBook 978-1-4832-9914-3

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COLT '91: Proceedings of the Fourth Annual Workshop on Computational Learning Theory covers the papers presented at the Fourth Workshop on Computational Learning Theory, held at the University of California at Santa Cruz on August 5-7, 1991. The book focuses on quantitative theories of machine learning. The selection first offers information on the role of learning in autonomous robots; tracking drifting concepts using random examples; investigating the distribution assumptions in the PAC learning model; and simultaneous learning of concepts and simultaneous estimation of probabilities.The text then examines the calculation of the learning curve of Bayes optimal classification algorithm for learning a perceptron with noise and a geometric approach to threshold circuit complexity. The manuscript takes a look at learning curves in large neural networks, learnability of infinitary regular sets, and learning monotone DNF with an incomplete membership oracle. Topics include monotone DNF learning algorithm, difficulties in learning infinitary regular sets, learning of a perception rule, and annealed approximation. The book also examines the fast identification of geometric objects with membership queries and a loss bound model for on-line stochastic prediction strategies. The selection is a valuable source of information for researchers interested in the computational learning theory.

Neural Network PC Tools

• 1st Edition
• October 28, 1990
• Russell C. Eberhart
• English
• eBook 978-1-4832-9700-2

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This is the first practical guide that enables you to actually work with artificial neural networks on your personal computer. It provides basic information on neural networks, as well as the following special features:

Languages, Compilers and Run-time Environments for Distributed Memory Machines

• 1st Edition
• Volume 3
• March 15, 1990
• J. Saltz + 1 more
• English
• eBook 978-1-4832-9538-1

  9 7 8 - 1 - 4 8 3 2 - 9 5 3 8 - 1

Papers presented within this volume cover a wide range of topics related to programming distributed memory machines. Distributed memory architectures, although having the potential to supply the very high levels of performance required to support future computing needs, present awkward programming problems. The major issue is to design methods which enable compilers to generate efficient distributed memory programs from relatively machine independent program specifications. This book is the compilation of papers describing a wide range of research efforts aimed at easing the task of programming distributed memory machines.