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Innovative Teaching and Learning

Knowledge-Based Paradigms

  • Book
  • © 2000

Overview

Editors:
  1. Lakhmi C. Jain

    1. KES Centre, University of South Australia, Adelaide, Mawson Lakes, Australia

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  • Useful help to develop successful learning and teaching tools for the teaching of knowledge-based paradigms

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 36)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xii
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  2. An Introduction to Innovative Teaching and Learning

    • D. Tedman, L. C. Jain
    Pages 1-30

  3. Teaching and Learning the AI Modeling

    • R. S. T. Lee, J. N. K. Liu
    Pages 31-86

  4. Artificial Intelligence Techniques for an Interdisciplinary Science Course

    • C. L. Karr, C. Sunal, C. Smith
    Pages 87-104

  5. On the Architecture of Intelligent Tutoring Systems and Its Application to a Neural Networks Course

    • J. F. Vega-Riveros
    Pages 105-134

  6. Teaching Knowledge Modeling at the Graduate Level — A Case Study

    • V. Devedžić
    Pages 135-188

  7. Innovative Modeling Techniques for Intelligent Tutoring Systems

    • V. Devedžić, D. Radović, L. Jerinić
    Pages 189-234

  8. Teaching Course on Artificial Neural Networks

    • J. Fulcher
    Pages 235-260

  9. Innovative Education for Fuzzy Logic Stabilization of Electric Power Systems in a Matlab/Simulink Environment

    • T. Hiyama
    Pages 261-287

  10. A Neural Network Workbench for Teaching and Learning

    • W. L. Goh, S. K. Amarasinghe
    Pages 289-309

  11. PRAM: A Courseware System for the Automatic Assessment of AI Programs

    • C. A. Higgins, F. Z. Mansouri
    Pages 311-329

  12. Back Matter

    Pages 331-335
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Keywords

About this book

Presented are innovative teaching and learning techniques for the teaching of knowledge-based paradigms. The main knowledge-based intelligent paradigms are expert systems, artificial neural networks, fuzzy systems and evolutionary computing. Expert systems are designed to mimic the performance of biological systems. Artificial neural networks can mimic the biological information processing mechanism in a very limited sense. Evolutionary computing algorithms are used for optimization applications, and fuzzy logic provides a basis for representing uncertain and imprecise knowledge.

Editors and Affiliations

  • KES Centre, University of South Australia, Adelaide, Mawson Lakes, Australia

    Lakhmi C. Jain

Bibliographic Information

  • Book Title: Innovative Teaching and Learning

  • Book Subtitle: Knowledge-Based Paradigms

  • Editors: Lakhmi C. Jain

  • Series Title: Studies in Fuzziness and Soft Computing

  • DOI: https://doi.org/10.1007/978-3-7908-1868-0

  • Publisher: Physica Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Physica-Verlag Heidelberg 2000

  • Hardcover ISBN: 978-3-7908-1246-6Published: 17 November 1999

  • Softcover ISBN: 978-3-7908-2465-0Published: 21 October 2010

  • eBook ISBN: 978-3-7908-1868-0Published: 11 November 2013

  • Series ISSN: 1434-9922

  • Series E-ISSN: 1860-0808

  • Edition Number: 1

  • Number of Pages: XII, 334

  • Topics: Artificial Intelligence, IT in Business

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