Hiroaki Kitano

Vipin Kumar

Laveen N. Kanal

Christian B. Suttner

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

Paruchuri R. Krishnaiah

[Anonymus AC00250467]

[Anonymus AC00652895]

Laveen N.. Kanal

Discriminant analysis for time series; Optimum rules for classification into two multivariate normal populations with the same covariance matrix;Large sample approximations and asymptotic expansions of classification statistics; Bayesian discrimination; Classification of growth curves; Nonparametric classification; Logistic discrimination; Nearest neighbor methods in discrimination; The classification and mixture maximum likelihood approaches to cluster analysis; Graphical techniques for multivariate data and for clustering; Cluster analysis software; Single-link clustering algorithms; Theory of multidimensional scaling; Multidimensional scaling and its applications; Intrinsic dimensionality extraction; Intrinsic dimensionality extraction; Structural methods in image analysis and recognition; Image models; Image texture survey; Applications of stochastic languages; A unifying viewpoint on pattern recognition; Logical functions in the problems of empirical prediction; Inference and data tables with missing values; Recognition of electrocardiographic patterns; Waveform parsing systems ...

Classification, Pattern Recognition, and Reduction of Dimensionality

P.S. Gopalakrishnan

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.

Parallel Algorithms for Machine Intelligence and Vision

Laveen N. Kanal

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