Parallel Processing for Artificial Intelligence 2

Parallel Processing for Artificial Intelligence 2

Author: V. Kumar

Publisher: Elsevier

Published: 2014-06-28

Total Pages: 248

ISBN-13: 1483295753

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With the increasing availability of parallel machines and the raising of interest in large scale and real world applications, research on parallel processing for Artificial Intelligence (AI) is gaining greater importance in the computer science environment. Many applications have been implemented and delivered but the field is still considered to be in its infancy. This book assembles diverse aspects of research in the area, providing an overview of the current state of technology. It also aims to promote further growth across the discipline. Contributions have been grouped according to their subject: architectures (3 papers), languages (4 papers), general algorithms (6 papers), and applications (5 papers). The internationally sourced papers range from purely theoretical work, simulation studies, algorithm and architecture proposals, to implemented systems and their experimental evaluation. Since the book is a second volume in the parallel processing for AI series, it provides a continued documentation of the research and advances made in the field. The editors hope that it will inspire readers to investigate the possiblities for enhancing AI systems by parallel processing and to make new discoveries of their own!


Book Synopsis Parallel Processing for Artificial Intelligence 2 by : V. Kumar

Download or read book Parallel Processing for Artificial Intelligence 2 written by V. Kumar and published by Elsevier. This book was released on 2014-06-28 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increasing availability of parallel machines and the raising of interest in large scale and real world applications, research on parallel processing for Artificial Intelligence (AI) is gaining greater importance in the computer science environment. Many applications have been implemented and delivered but the field is still considered to be in its infancy. This book assembles diverse aspects of research in the area, providing an overview of the current state of technology. It also aims to promote further growth across the discipline. Contributions have been grouped according to their subject: architectures (3 papers), languages (4 papers), general algorithms (6 papers), and applications (5 papers). The internationally sourced papers range from purely theoretical work, simulation studies, algorithm and architecture proposals, to implemented systems and their experimental evaluation. Since the book is a second volume in the parallel processing for AI series, it provides a continued documentation of the research and advances made in the field. The editors hope that it will inspire readers to investigate the possiblities for enhancing AI systems by parallel processing and to make new discoveries of their own!

Parallel Processing for Artificial Intelligence

Parallel Processing for Artificial Intelligence

Author: V. Kumar

Publisher:

Published: 1985

Total Pages: 12

ISBN-13:

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Book Synopsis Parallel Processing for Artificial Intelligence by : V. Kumar

Download or read book Parallel Processing for Artificial Intelligence written by V. Kumar and published by . This book was released on 1985 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Parallel Processing for Artificial Intelligence

Parallel Processing for Artificial Intelligence

Author: Laveen N. Kanal

Publisher:

Published: 1994

Total Pages: 256

ISBN-13:

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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.


Book Synopsis Parallel Processing for Artificial Intelligence by : Laveen N. Kanal

Download or read book Parallel Processing for Artificial Intelligence written by Laveen N. Kanal and published by . This book was released on 1994 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Parallel Processing for Artificial Intelligence 1

Parallel Processing for Artificial Intelligence 1

Author: L.N. Kanal

Publisher: Elsevier

Published: 2014-06-28

Total Pages: 445

ISBN-13: 1483295745

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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.


Book Synopsis Parallel Processing for Artificial Intelligence 1 by : L.N. Kanal

Download or read book Parallel Processing for Artificial Intelligence 1 written by L.N. Kanal and published by Elsevier. This book was released on 2014-06-28 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Parallel Computation and Computers for Artificial Intelligence

Parallel Computation and Computers for Artificial Intelligence

Author: J.S. Kowalik

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 305

ISBN-13: 1461319897

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It has been widely recognized that artificial intelligence computations offer large potential for distributed and parallel processing. Unfortunately, not much is known about designing parallel AI algorithms and efficient, easy-to-use parallel computer architectures for AI applications. The field of parallel computation and computers for AI is in its infancy, but some significant ideas have appeared and initial practical experience has become available. The purpose of this book has been to collect in one volume contributions from several leading researchers and pioneers of AI that represent a sample of these ideas and experiences. This sample does not include all schools of thought nor contributions from all leading researchers, but it covers a relatively wide variety of views and topics and in this sense can be helpful in assessing the state ofthe art. We hope that the book will serve, at least, as a pointer to more specialized literature and that it will stimulate interest in the area of parallel AI processing. It has been a great pleasure and a privilege to cooperate with all contributors to this volume. They have my warmest thanks and gratitude. Mrs. Birgitta Knapp has assisted me in the editorial task and demonstrated a great deal of skill and patience. Janusz S. Kowalik vii INTRODUCTION Artificial intelligence (AI) computer programs can be very time-consuming.


Book Synopsis Parallel Computation and Computers for Artificial Intelligence by : J.S. Kowalik

Download or read book Parallel Computation and Computers for Artificial Intelligence written by J.S. Kowalik and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: It has been widely recognized that artificial intelligence computations offer large potential for distributed and parallel processing. Unfortunately, not much is known about designing parallel AI algorithms and efficient, easy-to-use parallel computer architectures for AI applications. The field of parallel computation and computers for AI is in its infancy, but some significant ideas have appeared and initial practical experience has become available. The purpose of this book has been to collect in one volume contributions from several leading researchers and pioneers of AI that represent a sample of these ideas and experiences. This sample does not include all schools of thought nor contributions from all leading researchers, but it covers a relatively wide variety of views and topics and in this sense can be helpful in assessing the state ofthe art. We hope that the book will serve, at least, as a pointer to more specialized literature and that it will stimulate interest in the area of parallel AI processing. It has been a great pleasure and a privilege to cooperate with all contributors to this volume. They have my warmest thanks and gratitude. Mrs. Birgitta Knapp has assisted me in the editorial task and demonstrated a great deal of skill and patience. Janusz S. Kowalik vii INTRODUCTION Artificial intelligence (AI) computer programs can be very time-consuming.

Parallel Processing and Artificial Intelligence

Parallel Processing and Artificial Intelligence

Author: Mike Reeve

Publisher:

Published:

Total Pages: 307

ISBN-13: 9780783763934

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Book Synopsis Parallel Processing and Artificial Intelligence by : Mike Reeve

Download or read book Parallel Processing and Artificial Intelligence written by Mike Reeve and published by . This book was released on with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Parallel Processing for Artificial Intelligence 3

Parallel Processing for Artificial Intelligence 3

Author: J. Geller

Publisher: Elsevier

Published: 1997-02-10

Total Pages: 357

ISBN-13: 0080553826

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The third in an informal series of books about parallel processing for Artificial Intelligence, this volume is based on the assumption that the computational demands of many AI tasks can be better served by parallel architectures than by the currently popular workstations. However, no assumption is made about the kind of parallelism to be used. Transputers, Connection Machines, farms of workstations, Cellular Neural Networks, Crays, and other hardware paradigms of parallelism are used by the authors of this collection. The papers arise from the areas of parallel knowledge representation, neural modeling, parallel non-monotonic reasoning, search and partitioning, constraint satisfaction, theorem proving, parallel decision trees, parallel programming languages and low-level computer vision. The final paper is an experience report about applications of massive parallelism which can be said to capture the spirit of a whole period of computing history. This volume provides the reader with a snapshot of the state of the art in Parallel Processing for Artificial Intelligence.


Book Synopsis Parallel Processing for Artificial Intelligence 3 by : J. Geller

Download or read book Parallel Processing for Artificial Intelligence 3 written by J. Geller and published by Elsevier. This book was released on 1997-02-10 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: The third in an informal series of books about parallel processing for Artificial Intelligence, this volume is based on the assumption that the computational demands of many AI tasks can be better served by parallel architectures than by the currently popular workstations. However, no assumption is made about the kind of parallelism to be used. Transputers, Connection Machines, farms of workstations, Cellular Neural Networks, Crays, and other hardware paradigms of parallelism are used by the authors of this collection. The papers arise from the areas of parallel knowledge representation, neural modeling, parallel non-monotonic reasoning, search and partitioning, constraint satisfaction, theorem proving, parallel decision trees, parallel programming languages and low-level computer vision. The final paper is an experience report about applications of massive parallelism which can be said to capture the spirit of a whole period of computing history. This volume provides the reader with a snapshot of the state of the art in Parallel Processing for Artificial Intelligence.

Deep Learning and Parallel Computing Environment for Bioengineering Systems

Deep Learning and Parallel Computing Environment for Bioengineering Systems

Author: Arun Kumar Sangaiah

Publisher: Academic Press

Published: 2019-07-26

Total Pages: 280

ISBN-13: 0128172932

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Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas. Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data


Book Synopsis Deep Learning and Parallel Computing Environment for Bioengineering Systems by : Arun Kumar Sangaiah

Download or read book Deep Learning and Parallel Computing Environment for Bioengineering Systems written by Arun Kumar Sangaiah and published by Academic Press. This book was released on 2019-07-26 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas. Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data

Parallel Processing for Supercomputers and Artificial Intelligence

Parallel Processing for Supercomputers and Artificial Intelligence

Author:

Publisher:

Published: 1989

Total Pages: 673

ISBN-13: 9780071007498

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Book Synopsis Parallel Processing for Supercomputers and Artificial Intelligence by :

Download or read book Parallel Processing for Supercomputers and Artificial Intelligence written by and published by . This book was released on 1989 with total page 673 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Parallel Algorithms for Machine Intelligence and Vision

Parallel Algorithms for Machine Intelligence and Vision

Author: Vipin Kumar

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 445

ISBN-13: 1461233909

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Recent research results in the area of parallel algorithms for problem solving, search, natural language parsing, and computer vision, are brought together in this book. The research reported demonstrates that substantial parallelism can be exploited in various machine intelligence and vision problems. The chapter authors are prominent researchers actively involved in the study of parallel algorithms for machine intelligence and vision. Extensive experimental studies are presented that will help the reader in assessing the usefulness of an approach to a specific problem. Intended for students and researchers actively involved in parallel algorithms design and in machine intelligence and vision, this book will serve as a valuable reference work as well as an introduction to several research directions in these areas.


Book Synopsis Parallel Algorithms for Machine Intelligence and Vision by : Vipin Kumar

Download or read book Parallel Algorithms for Machine Intelligence and Vision written by Vipin Kumar and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent research results in the area of parallel algorithms for problem solving, search, natural language parsing, and computer vision, are brought together in this book. The research reported demonstrates that substantial parallelism can be exploited in various machine intelligence and vision problems. The chapter authors are prominent researchers actively involved in the study of parallel algorithms for machine intelligence and vision. Extensive experimental studies are presented that will help the reader in assessing the usefulness of an approach to a specific problem. Intended for students and researchers actively involved in parallel algorithms design and in machine intelligence and vision, this book will serve as a valuable reference work as well as an introduction to several research directions in these areas.