Parallel Processing for Artificial Intelligence 1

Parallel Processing for Artificial Intelligence 1

1st Edition - February 12, 1989

Write a review

  • Editors: L.N. Kanal, H. Kitano, V. Kumar, C.B. Suttner
  • eBook ISBN: 9781483295749

Purchase options

Purchase options
DRM-free (PDF)
Sales tax will be calculated at check-out

Institutional Subscription

Free Global Shipping
No minimum order


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.

Table of Contents

  • Preface. Editors. Authors. Image Processing. A perspective on parallel processing in computer vision and image understanding (A. Choudhary, S. Ranka). On supporting rule-based image interpretation using a distributed memory multicomputer (C.-C. Chu, J. Ghosh, J.K. Aggarwal). Parallel affine image warping (G. Gusciora, J.A. Webb). Image processing on reconfigurable meshes with buses (J.-F. Jenq, S. Sahni). Semantic Networks. Inheritance operations in massively parallel knowledge representation (J. Geller). Providing computationally effective knowledge representation via massive parallelism (M.P. Evett, W.A. Anderson, J.A. Hendler). Production Systems. Speeding up production systems: from concurrent matching to parallel rule firing (J.N. Amaral, J. Ghosh). Guaranteeing serializability in parallel production systems (J.G. Schmolze). Mechanization of Logic. Parallel automated theorem proving (C.B. Suttner, J.M. Schumann). Massive parallelism in inference systems (F. Kurfess). Representing propositional logic and searching for satisfiability in connectionist networks (G. Pinkas). Constraint Satisfaction. Parallel and distributed finite constraint satisfaction: complexity, algorithms and experiments (Y. Zhang, A.K. Mackworth). Parallel algorithms and architectures for consistent labeling (W.-M. Lin, V.K. Prasanna). Other Topics. Massively parallel parsing algorithms for natural language (M.A. Palis, D.S.L. Wei). Process trellis and FGP: software architectures for data filtering and mining (M. Factor, S.J. Fertig, D.H. Gelernter).

Product details

  • No. of pages: 443
  • Language: English
  • Copyright: © North Holland 1994
  • Published: February 12, 1989
  • Imprint: North Holland
  • eBook ISBN: 9781483295749

About the Editors

L.N. Kanal

Affiliations and Expertise

University of Maryland, Department of Computer Science, College Park, MD, USA

H. Kitano

Affiliations and Expertise

Sony Computer Science Laboratory, Tokyo, Japan

V. Kumar

Affiliations and Expertise

National Physical Laboratory, New Delhi, India

C.B. Suttner

Affiliations and Expertise

Institute of Informatics, Technical University of Munich, Germany

Ratings and Reviews

Write a review

There are currently no reviews for "Parallel Processing for Artificial Intelligence 1"