Parallelism and Programming in Classifier Systems
Published on 1. December 1990
213 pages
978-0-08-051355-3 (ISBN)
Description
Parallelism and Programming in Classifier Systems deals with the computational properties of the underlying parallel machine, including computational completeness, programming and representation techniques, and efficiency of algorithms. In particular, efficient classifier system implementations of symbolic data structures and reasoning procedures are presented and analyzed in detail. The book shows how classifier systems can be used to implement a set of useful operations for the classification of knowledge in semantic networks. A subset of the KL-ONE language was chosen to demonstrate these operations. Specifically, the system performs the following tasks: (1) given the KL-ONE description of a particular semantic network, the system produces a set of production rules (classifiers) that represent the network; and (2) given the description of a new term, the system determines the proper location of the new term in the existing network. These two parts of the system are described in detail. The implementation reveals certain computational properties of classifier systems, including completeness, operations that are particularly natural and efficient, and those that are quite awkward. The book shows how high-level symbolic structures can be built up from classifier systems, and it demonstrates that the parallelism of classifier systems can be exploited to implement them efficiently. This is significant since classifier systems must construct large sophisticated models and reason about them if they are to be truly ""intelligent."" Parallel organizations are of interest to many areas of computer science, such as hardware specification, programming language design, configuration of networks of separate machines, and artificial intelligence This book concentrates on a particular type of parallel organization and a particular problem in the area of AI, but the principles that are elucidated are applicable in the wider setting of computer science.
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12/1990
Morgan Kaufmann
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Content
List of FiguresList of AppendicesPreface1 Introduction 1.1 Parallelism and Classifier Systems 1.2 Classification and KL-ONE 1.3 Subsymbolic Models of Intelligence 1.4 Overview 2 Background Information 2.1 Parallelism 2.1.1 Coarse-Grained Parallelism 2.1.2 Fine-Grained Parallelism and Emergent Computation 2.2 Classifier Systems 2.3 KL-ONE 2.3.1 Overview of KL-ONE 2.3.2 Description of Implemented KL-ONE Subset 2.3.3 Literature Review for KL-ONE 2.4 Summary 3 Approach 3.1 Implementation 3.2 Evaluation 3.3 Summary 4 Classifier Systems 4.1 Computational Properties of Classifier Systems 4.2 Classifier System Algorithms 4.2.1 Boolean Queries 4.2.2 Overhead Classifiers 4.2.3 Set Union 4.2.4 Set Intersection 4.2.5 Set Complementation 4.2.6 Set Difference 4.2.7 Memory Operations 4.2.8 Arithmetic Operations 4.2.9 Finding Maximum and Minimum Values 4.2.10 Comparison of Two Numbers 4.2.11 Binary Addition 4.2.12 Inheritance 4.3 Summary 5 Classifier System Implementation of KL-ONE 5.1 Representation 5.1.1 Overview 5.1.2 Detailed Description 5.1.3 Concepts and Roles 5.1.4 Primitives 5.1.5 Concept Specialization and Role Differentiation 5.1.6 Value Restrictions 5.1.7 Number Restrictions 5.1.8 Role Value Maps 5.2 Algorithms 5.2.1 Most Specific Subsumers 5.2.2 Concept Subsumption 5.2.3 Value Restrictions 5.2.4 Number Restrictions 5.2.5 Role Value Map Subsumption 5.3 Summary 6 Analysis of Results 6.1 Time of Computation 6.2 Number and Size of Processors 6.3 Inter-Processor Communication 6.4 Comparison with Sequential Algorithm 6.5 Computational Tradeoffs 6.6 Summary of Results 7 Conclusions 7.1 Classifier Systems 7.2 KL-ONE 7.3 Parallelism Appendices Bibliography
