Parallelism and Programming in Classifier Systems.

By: Forrest, StephanieMaterial type: TextTextSeries: eBooks on DemandResearch Notes in Artificial Intelligence: Publisher: Saint Louis : Elsevier Science, 2014Description: 1 online resource (224 p.)ISBN: 9780080513553Subject(s): Parallel processing (Electronic computers) | Parallel programming (Computer science)Genre/Form: Electronic books.Additional physical formats: Print version:: Parallelism and Programming in Classifier SystemsDDC classification: 005.2 LOC classification: QA76.58 -- .F66 1990ebOnline resources: Click here to view this ebook.
Contents:
Front Cover -- Parallelism and Programming in Classifier Systems -- Copyright Page -- Table of Contents -- Dedication -- List of Figures -- List of Appendices -- Preface -- Chapter 1. Introduction -- 1.1 Parallelism and Classifier Systems -- 1.2 Classification and KL-ONE -- 1.3 Subsymbolic Models of Intelligence -- 1.4 Overview -- Chapter 2. Background Information -- 2.1 Parallelism -- 2.2 Classifier Systems -- 2.3 KL-ONE -- 2.4 Summary -- Chapter 3. Approach -- 3.1 Implementation -- 3.2 Evaluation -- 3.3 Summary -- Chapter 4. Classifier Systems
4.1 Computational Properties of Classifier Systems -- 4.2 Classifier System Algorithms -- 4.3 Summary -- Chapter 5. Classifier System Implementation of KL-ONE -- 5.1 Representation -- 5.2 Algorithms -- 5.3 Summary -- Chapter 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 -- Chapter 7. Conclusions -- 7.1 Classifer Systems -- 7.2 KL-ONE -- 7.3 Parallelism -- APPENDICES: Backus Normal Form Description of Input Language
Bibliography
Summary: 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 the
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QA76.58 .F66 2014 (Browse shelf) http://uttyler.eblib.com/patron/FullRecord.aspx?p=1876686 Available EBL1876686

Front Cover -- Parallelism and Programming in Classifier Systems -- Copyright Page -- Table of Contents -- Dedication -- List of Figures -- List of Appendices -- Preface -- Chapter 1. Introduction -- 1.1 Parallelism and Classifier Systems -- 1.2 Classification and KL-ONE -- 1.3 Subsymbolic Models of Intelligence -- 1.4 Overview -- Chapter 2. Background Information -- 2.1 Parallelism -- 2.2 Classifier Systems -- 2.3 KL-ONE -- 2.4 Summary -- Chapter 3. Approach -- 3.1 Implementation -- 3.2 Evaluation -- 3.3 Summary -- Chapter 4. Classifier Systems

4.1 Computational Properties of Classifier Systems -- 4.2 Classifier System Algorithms -- 4.3 Summary -- Chapter 5. Classifier System Implementation of KL-ONE -- 5.1 Representation -- 5.2 Algorithms -- 5.3 Summary -- Chapter 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 -- Chapter 7. Conclusions -- 7.1 Classifer Systems -- 7.2 KL-ONE -- 7.3 Parallelism -- APPENDICES: Backus Normal Form Description of Input Language

Bibliography

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 the

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