Natural Artificial Parallel Computation
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Parallel Processing for Artificial Intelligence
Parallel processing for AI problems is of great current interest because of its potential for alleviating the computational demands of AI procedures. The articles in this book consider parallel processing for problems in several areas of artificial intelligence: image processing, knowledge representation in semantic networks, production rules, mechanization of logic, constraint satisfaction, parsing of natural language, data filtering and data mining. The publication is divided into six sections. The first addresses parallel computing for processing and understanding images. The second discusses parallel processing for semantic networks, which are widely used means for representing knowledge - methods which enable efficient and flexible processing of semantic networks are expected to have high utility for building large-scale knowledge-based systems. The third section explores the automatic parallel execution of production systems, which are used extensively in building rule-based expert systems - systems containing large numbers of rules are slow to execute and can significantly benefit from automatic parallel execution. The exploitation of parallelism for the mechanization of logic is dealt with in the fourth section. While sequential control aspects pose problems for the parallelization of production systems, logic has a purely declarative interpretation which does not demand a particular evaluation strategy. In this area, therefore, very large search spaces provide significant potential for parallelism. In particular, this is true for automated theorem proving. The fifth section considers the problem of constraint satisfaction, which is a useful abstraction of a number of important problems in AI and other fields of computer science. It also discusses the technique of consistent labeling as a preprocessing step in the constraint satisfaction problem. Section VI consists of two articles, each on a different, important topic. The first discusses parallel formulation for the Tree Adjoining Grammar (TAG), which is a powerful formalism for describing natural languages. The second examines the suitability of a parallel programming paradigm called Linda, for solving problems in artificial intelligence. Each of the areas discussed in the book holds many open problems, but it is believed that parallel processing will form a key ingredient in achieving at least partial solutions. It is hoped that the contributions, sourced from experts around the world, will inspire readers to take on these challenging areas of inquiry.
Natural and Artificial Parallel Computation
These eleven contributions by leaders in the fields of neuroscience, artificial intelligence, and cognitive science cover the phenomenon of parallelism in both natural and artificial systems, from the neural architecture of the human brain to the electronic architecture of parallel computers.The brain's complex neural architecture not only supports higher mental processes, such as learning, perception, and thought, but also supervises the body's basic physiological operating system and oversees its emergency services of damage control and self-repair. By combining sound empirical observation with elegant theoretical modeling, neuroscientists are rapidly developing a detailed and convincing account of the organization and the functioning of this natural, living parallel machine. At the same time, computer scientists and engineers are devising imaginative parallel computing machines and the programming languages and techniques necessary to use them to create superb new experimental instruments for the study of all parallel systems.Michael A. Arbib is Professor of Computer Science, Neurobiology, and Physiology at the University of Southern California. J. Alan Robinson is University Professor at Syracuse University.Contents: Natural and Artificial Parallel Computation, M. A. Arbib, J. A. Robinson. The Evolution of Computing, R. E. Gomory. The Nature of Parallel Programming, P. Brinch Hansen. Toward General Purpose Parallel Computers, D. May. Applications of Parallel Supercomputers, G. E. Fox. Cooperative Computation in Brains and Computers, M. A. Arbib. Parallel Processing in the Primate Cortex, P. Goldman-Rakic. Neural Darwinism, G. M. Edelman, G. N. Reeke, Jr. How the Brain Rewires Itself, M. Merzenich. Memory-Based Reasoning, D. Waltz. Natural and Artificial Reasoning, J. A. Robinson.
Adaptive and Natural Computing Algorithms
Author: Andrej Dobnikar
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-03-03
The two-volume set LNCS 6593 and 6594 constitutes the refereed proceedings of the 10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The 83 revised full papers presented were carefully reviewed and selected from a total of 144 submissions. The first volume includes 42 papers and a plenary lecture and is organized in topical sections on neural networks and evolutionary computation.