Parallel processing for artificial intelligence

著者

書誌事項

Parallel processing for artificial intelligence

edited by L.N. Kanal, ... [et al.]

（Machine intelligence and pattern recognition, v. 14, 15, 20）

North-Holland, 1994-

  • v. 1
  • v. 2
  • v. 3

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注記

Collection of papers from the IJCAI-93, held in Chambery, France

V. 2: edited by Hiroaki Kitano, Vipin Kumar, Christian B. Suttner

Includes bibliographical references

内容説明・目次

巻冊次

v. 1 ISBN 9780444817044

内容説明

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.

目次

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).
巻冊次

v. 2 ISBN 9780444818379

内容説明

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!

目次

Architectures. Hybrid systems on a multi-grain parallel architecture (G. Ouvradou, A.P. Maubant, A. Thepaut). An abstract machine for implementing connectionist and hybrid systems on multi-processor architectures (Y. Lallement, T. Cornu, S. Vialle). A dense, massively parallel architecture (T. Reski, W.B. Strothmann). Languages. Using confluence to control parallel production systems (J.G. Schmolze, W. Snyder). Toward an architecture independent high level parallel programming model for artificial intelligence (M.S. Berlin). An object-oriented approach for programming the connection machine (L. Hotz). Automatic parallelisation of LISP programs (E. Furse, K.H. Sewell). General Algorithms. Simulation analysis of static partitioning with slackness (C.B. Suttner, M.R. Jobmann). Parallel distributed realization for constraint satisfaction (W. Hower, S. Jacobi). A first step towards the massively parallel game-tree search: a SIMD approach (V.-D. Cung, L. Gotte). Initialization of parallel branch-and-bound algorithms (D. Henrich). A model for parallel deduction (U. Assmann). Applications. Toward real-time motion planning (D.J. Challou, M. Gini, V. Kumar). Toward massively parallel spoken language translation (K. Oi, E. Sumita, O. Furuse, H. Iida, H. Kitano). Weather forecasting using memory-based reasoning (T. Mohri, M. Nakamura, H. Tanaka). Scalability of an OR-parallel theorem prover on a network of transputer - a modelling approach (J. Schumann, M. Jobmann). A coarse grained parallel induction heuristic (R.A. Pearson). Fuzzy logic controlled dynamic allocation system (K. Stoffel, I. Law, B. Hirsbrunner).
巻冊次

v. 3 ISBN 9780444824868

内容説明

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.

目次

Knowledge Representation Massively parallel knowledge representation and reasoning: taking a cue from the brain (L. Shastri, D.R. Mani). Massively parallel support for nonmonotonic reasoning (B. Boutsinas et al.). Parallel operations on class hierarchies with double strand representation (E.Y. Lee, J. Geller). PARKA on MIMD-Supercomputers (K. Stoffel, et al.). Search and Partitioning A hybrid approach to improving the performance of parallel search (D.J. Cooke). Static partitioning with slackness. (C.B. Suttner). Problem partition and solvers coordination in distributed constraint satisfaction (P. Berlandier, B. Neveu). Theorem Proving Parallel propagation in the description-logic system FLEX (F.W. Bergmann, J.J. Quantz). An alternative approach to concurrent theorem-proving (M. Fisher). SiCoTHEO - simple competitive parallel theorem provers based on SETHEO (J. Schumann). Miscellaneous Low-Level computer vision algorithms: performance evaluation on parallel and distributed architectures (G. Destri, P. Marenzoni). Decision trees on parallel processors (R. Kufrin). Application development under ParCeL-1 (Y. Lallement et al.). AI applications of massive parallelism: an experience report (D.L. Waltz). Appendix

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