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Computational Models of Learning

  • Book
  • © 1987

Overview

Editors:
  1. Leonard Bolc

    1. Institute of Informatics, Warsaw University, Warszawa, Poland

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Part of the book series: Symbolic Computation (SYMBOLIC)

Part of the book sub series: Artificial Intelligence (1064)

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Table of contents (5 chapters)

  1. Front Matter

    Pages I-IX
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  2. Learning Strategies and Automated Knowledge Acquisition

    • Ryszard S. Michalski
    Pages 1-19

  3. Heuristics for Empirical Discovery

    • Pat Langley, Herbert A. Simon, Gary L. Bradshaw
    Pages 21-54

  4. Transfer of Training in Procedural Learning A Matter of Conjectures and Refutations?

    • Stellan Ohlsson
    Pages 55-88

  5. Conceptual Knowledge Acquisition in Search

    • Larry Rendell
    Pages 89-159

  6. Cognitive Development as Optimisation

    • J. G. Wolff
    Pages 161-205

  7. Back Matter

    Pages 207-208
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Keywords

About this book

In recent years, machine learning has emerged as a significant area of research in artificial intelligence and cognitive science. At present, research in the field is being intensified from both the point of view of theory and of implementation, and the results are being introduced in practice. Machine learning has recently become the subject of interest of many young and talented scientists whose bold ideas have greatly contributed to the broadening of knowledge in this rapidly developing field of science. This situation has manifested itself in an increasing number of valuable contributions to scientific journals. However, such papers are necessarily compact descriptions of research problems. Computational Models of Learning supplements these contributions and is a collection of more extensive essays. These essays provide the reader with an increased knowledge of carefully selected problems of machine learning.

Editors and Affiliations

  • Institute of Informatics, Warsaw University, Warszawa, Poland

    Leonard Bolc

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