Parallel processing for artificial intelligence

419 Pages·1994·18.897 MB·English

by  Laveen N Kanal

Machine Intelligence and
Pattern Recognition
Volume 14
Series Editors
L.N. KANAL
and
A. ROSENFELD
University of Maryland
College Park, Maryland, U.S.A.
NORTH-HOLLAND
AMSTERDAM · LONDON · NEW YORK · TOKYO
Parallel Processing
for Artificial Intelligence 1
Edited by
Laveen N. KANAL
University of Maryland
College Park, Maryland, U.S.A.
Vipin KUMAR
University of Minnesota
Minneapolis, Minnesota, U.S.A.
Hiroaki KITANO
Sony Computer Science Laboratory, Japan
and Carnegie Mellon University
Pittsburgh, Pennsylvania, U.S.A.
Christian B. SUTTNER
Technical University of Munich
Munich, Germany
1994
NORTH-HOLLAND
AMSTERDAM · LONDON · NEW YORK · TOKYO
ELSEVIER SCIENCE B.V.
Sara Burgerhartstraat 25
P.O. Box 211, 1000 AE Amsterdam, The Netherlands
Library of Congress Cataloglng-ln-PublIcatIon Data
Parallel processing for artificial intelligence / edited by L.N.
Kanal, ... [et al.].
p. cm. — (Machine Intelligence and pattern recognition ; v.
14-15)
Collection of papers from the IJCAI-93, held In Chambery, France.
Includes bibliographical references.
ISBN 0-444-81704-2 (v. 1). — ISBN 0-444-81837-5 (v. 2)
1. Parallel processing (Electronic computers) 2. Artificial
Intelligence. I. Kanal, Laveen Ν. II. International Joint
Conference on Artificial Intelligence (1993 : Chambery, France)
III. Series: Machine Intelligence and pattern recognition ; v.
14-<15 >.
QA76.58.P37775 1994
006.3—dc20 94-15133
CIP
ISBN: 0 444 81704 2
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PREFACE
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 articles are grouped into six sections. The first four papers address parallel
computing for processing and understanding images. Choudhary and Ranka set the
stage for this section, titled Image Processing, by examining, as an example, the
computationally intensive nature of feature extraction in image processing. They note
that vision systems involve low-level, medium-level and high-level operations and
require integrating algorithms for image processing, numerical analyis, graph theory,
AI and databases. They discuss issues of mesh and pyramid architectures, spatial and
temporal parallelism, local and global communication, data decomposition and load
balancing and the status of architectures, programming models and software tools in
parallel computing for computer vision and image understanding.
The second article, by Chu, Gosh, and Aggarwal, focusses on high-level operations
for image understanding and reports on a parallel implementation of a rule-based image
interpretation system on a distributed memory architecture machine with message
passing. The authors discuss the results of their experimental investigation of this
implementation from the perspectives of data access locality and task granularity.
The next article by Gusciora and Webb presents results of their investigation of several
methods for parallel affine image warping on a linear processor array. Evaluating the
efficiency, capability, and memory use of the various methods, the authors describe
and recommend a method which they call "sweep-based" warping.
In the final article of Section I, Jenq and Sahni examine a class of parallel computers
called reconfigurable mesh with bus (RMB). They present fast algorithms for template
matching, clustering, erosion and dilation, and area and perimeter computation of
image components for some members of the RMB family of architectures.
The articles in Section II discuss parallel processing for semantic networks. Semantic
networks are a widely used means for representing knowledge; methods which enable
efficient and flexible processing of semantic networks are expected to have high utility
vi
for building large-scale knowledge-based systems.
Geller presents a method to speed up search in an inheritance tree. The method is
based on a parallel class tree representation that uses a list in combination with a
preorder number scheme. In addition to standard inheritance, a newly defined
operation, upward inductive inheritance, is introduced. The proposed methods exhibit
high performance which is largely independent of tree depth and branching factor.
The second article in this section, by Evett, Andersen, and Hendler, describes
PARKA, a frame-based knowledge representation system implemented on the
Connection Machine. PARKA performs recognition queries, finding all objects with
a set of specified properties in 0(d+m) time — proportional primarily to the depth d
of the knowledge base. They have tested PARKA's performance using knowledge
from MCC's Cyc commonsense knowledge base, and have obtained very high
performance.
Section III deals with the automatic parallel execution of production systems.
Production systems 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 article by Amaral and Ghosh provides a survey of the research on parallel
processing of production systems. It covers the early systems in which all productions
are matched in parallel but only one rule is fired at a time, as well as more recent
research in which different rules are fired concurrently. The article concludes with
observations on the overall effectiveness of these different approaches and suggestions
for further work.
The next article, by Schmölze, deals extensively with the problems involved in
concurrent firing of rules. The original production systems were designed for
executing one rule at a time. If multiple rules are executed simultaneously, then in
some cases, the parallel execution may lead to undesirable results. Schmölze presents
a framework for guaranteeing the serializable behavior in the parallel execution of
productions.
Section IV deals with the exploitation of parallelism for the mechanization of logic.
While sequential control aspects pose problems for the parallelization of production
systems (see Section III), logic has a purely declarative interpretation which does not
demand a particular evaluation strategy. Therefore, in this area, very large search
spaces provide a significant potential for parallelism. In particular, this is true for
automated theorem proving. The three articles in Section IV deal with parallel
deduction at various levels, ranging from coarse granular parallel deduction for
predicate logic to connectionist processing of propositional logic.
vii
Suttner and Schumann's article gives a comprehensive survey of the approaches for
parallel automated theorem proving for first order logic. It includes both implemented
and proposed systems, and briefly describes for each approach the underlying logic
calculus, the model of computation, and available experimental results, together with
an assessment. In addition the authors present a classification scheme for parallel
search based systems which leads to an adequate grouping of the approaches
investigated. The orthogonal distinctions proposed are partitioning versus competition
parallelization (with further subclasses), and cooperative versus uncooperative
parallelization. Together with an extensive list of references, the article provides a
classification and overview of the state-of-the-art and the history of parallel theorem
proving.
The article by Kurfess primarily provides an overview of the potential application of
parallelism in logic. The author first describes various levels at which parallelism can
be utilized, ranging from the formula, clause, literal, term, and symbol level down to
the subsymbolic level. He then focuses on the prospects of massive parallelism for
these levels, and discusses their potential regarding computation, communication,
synchronization, and memory. In this regard, the author proposes the "term level" as
the most promising. Finally, a fine-grain deductive system is described in a
connectionist setting, in which parallel unification is based on a matrix representation
of terms.
Pinkas describes a connectionist approach towards the mechanization of logic. His
method is based on a linear time transformation of propositional formulas into
symmetric connectionist networks representing quadratic energy functions. The global
minima of such a function are equivalent to the satisfying models of the propositional
formula. The author presents simulation results for randomly generated satisfiability
problems using three algorithms based on Hopfield networks, Boltzmann machines,
and Mean-Field networks, respectively. He also describes the application of the
approach for propositional inference and the incremental adaptation of energy functions
to represent changes in the knowledge base.
In the first article in Section V, Zhang and Mackworth consider the problem of
constraint satisfaction, which is a useful abstraction of a number of important problems
in AI and other fields of computer science. They present parallel formulations of some
well-known constraint satisfaction algorithms and analyze their performance
theoretically and experimentally. In particular they show that certain classes of
constraint satisfaction problems can be solved in polylog time using a polynomial
number of processors.
In the second article in this section, Lin and Prasanna discuss the technique of
consistent labeling as a preprocessing step in the constraint satisfaction problem and
present several parallel implementations for the technique.
viii
Section VI consists of two articles, each on a different, important topic. Palis and Wei
discuss parallel formulations for the Tree Adjoining Grammar (TAG), which is a
powerful formalism for describing natural languages. The serial complexity of TAG
parsing algorithms is much higher than the complexity of simpler grammars such as
context-free grammars. Hence it is important to be able to reduce the overall run time
using parallel computing. Palis and Wei present two parallel algorithms for TAG
parsing, and present theoretical and experimental results.
In the final article, Factor, Fertig, and Gelernter discuss the suitability of a parallel
programming paradigm called Linda for solving problems in artificial intelligence.
They introduce two software architectures, the FGP machine and the Process Trellis.
The FGP machine is a software architecture for a data-base driven expert system that
learns. Process Trellis is a software architecture for real time heuristic monitors.
Both of these systems have been implemented in the language Linda.
As is noted in the first article in the book, while some of the individual components
of parallelism in vision systems may be understood, the overall process of vision
remains very much an open problem. A similar remark applies to knowledge
representation, reasoning, problem solving, natural language processing and other
capabilities desired for machine intelligence. We think parallel processing will be a key
ingredient of (partial) solutions in all the above areas. We thank the authors for the
articles included here and hope their work inspires some readers to take on these
challenging areas of inquiry.
Laveen N. Kanal
College Park, MD
Vipin Kumar
Minnaepolis, MN
Hiroaki Kitano
Tokyo, Japan
Christian Suttner
Munich, Germany
xi
EDITORS
Laveen Ν. Kanal is a Professor of Computer Science at the University of Maryland,
College Park, and Managing Director of L Ν Κ Corporation, Inc. In 1972 he was
elected a Fellow of the IEEE and a Fellow of the American Association for the
Advancement of Science. In 1992 he was elected a Fellow of the American Association
for Artificial Intelligence and received the King-Sun Fu award of the International
Association for Pattern Recognition.
Vipin Kumar is currently an Associate Professor in the Department of Computer
Science at the University of Minnesota. His co-authored text book, Introduction to
Parallel Computing, was published in 1993 by Benjamin Cummings. He is on the
editorial board of IEEE Transactions on Data and Knowledge Engineering.
Hiroaki Kita no, Ph.D, is with Sony Computer Science Laboratory and Carnegie
Mellon University. In 1993 he received the Computers and Thought Award of the
International Joint Conference on Artificial Intelligence.
Christian Suttner is currently writing his doctoral dissertation and working on tools
and methods for the utilization of parallel computers at the TU München. With Laveen
Kanal he co-edited the Proceedings of a Workshop on Parallel Processing held in
Sydney, Australia in August 1991.
xiii
AUTHORS
J.K. Aggarwal is Cullen Professor of Electrical and Computer Engineering at The
University of Texas at Austin and Director of the Computer and Vision Research
Center. An IEEE Fellow, he is an Editor of IEEE Transactions on Parallel and
Distributed Systems. In 1992, Dr. Aggarwal received the Senior Research Award of
the American Society of Engineering Education.
José Nelson Amaral is a professor at Pontificia Universidade Catolica do Rio Grande
do Sul (PUCRS) - Brazil. He is currently working towards his Ph.D. at the Univ. of
Texas at Austin.
Alok Choudhary is an associate professor at Syracuse University. His research
interests include high-performance parallel and distributed computing, software
environments and applications. He received an NSF Young Investigator Award in
1993.
Chen-Chau Chu received the Ph.D. degree in electrical and computer engineering
from The University of Texas at Austin in 1991. He is currently employed by the
Schlumberger Austin Systems Center. His research interests include machine
intelligence, computer vision, and data modeling.
Matt Evett received his Ph.D. in computer science from the University of Maryland
in 1994, and joined the faculty at Florida Atlantic University in Boca Raton.
James Geller is an associate professor at the Computer and Information Sciences
Department of the New Jersey Institute of Technology. He has worked in artificial
intelligence and object-oriented databases and is currently involved in a large research
project on distance learning.
Joydeep Ghosh is currently an associate professor of electrical and computer
engineering at the University of Texas at Austin where he conducts research on
parallel, intelligent computer architectures and artificial neural systems. He received
the 1992 Darlington Award for best journal paper from the IEEE Circuits and Systems
Society.
xiv
George Gusciora expects to receive the Ph.D.in computer engineering from Carnegie
Mellon University in February 1994. He plans to work at the Maui High Performance
Computing Center in Hawaii. His research interests include parallel programming
tools, parallel computer architectures and parallel algorithms.
Jim Hendler is an associate professor and head of the Autonomous Mobile Robots
Laboratory at the University of Maryland. He serves as the Artificial Intelligence area
editor for the journal Connection Science is an associate editor of the Journal of
Experimental and Theoretical AI, and is on the editorial board of Autonomous Robots.
Jing-Fu Jeng, Ph.D., University of Minnesota, 1991, is currently an assistant
professor of physics, mathematics, and computer science at Tennessee State University
in Nashville, Tennessee. His research interests include parallel and distributed
processing algorithms, computer architectures and systems, and computer vision.
Franz J. Kurfess joined the Neural Information Processing Department at the
University of Ulm in 1992 after a stay as postdoctoral fellow with the International
Computer Science Institute in Berkeley. His main interest lies in combining artificial
intelligence, neural networks and parallel processing techniques, in particular for logic
and reasoning.
Wei-Ming Lin, Ph.D., University of Southern California, 1991, is currently an
assistant professor at the University of Texas at San Antonio. His research interests
include parallel and distributed computing, parallel processing for artificial intelligence,
image processing and computer vision.
Alan Mackworth is a professor of computer science at the University of British
Columbia and the Shell Canada Fellow of the AI and Robotics Program of the
Canadian Institute for Advanced Research. He currently serves as the Director of the
UBC Laboratory for Computational Intelligence. He is known for his work on
constraint satisfaction and its applications in perception, reasoning and situated robots
Michael A. Palis is currently an associate professor of electrical and computer
engineering at the New Jersey Institute of Technology. He is an editorial board
member of the IEEE Transactions on Computers, and subject area editor of the Journal
of Parallel and Distributed Computing.
Gadi Pinkas is currently director of research and development at Amdocs Inc., a
senior fellow of the Center for Optimization and Semantic Control and a senior
research associate at the Department of Computer Science at Washington University,
St. Louis, Missouri.