Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning

  • Susan Chipman
  • Alan L. Meyrowitz

Table of contents

  1. Front Matter
    Pages i-xi
  2. John R. Anderson, Albert T. Corbett
    Pages 1-24
  3. Kurt VanLehn, Randolph M. Jones
    Pages 25-82
  4. Scott B. Huffman, Douglas J. Pearson, John E. Laird
    Pages 209-244
  5. Kristian J. Hammond, Colleen M. Seifert
    Pages 245-267
  6. Paul S. Rosenbloom, Soowon Lee, Amy Unruh
    Pages 269-307
  7. Back Matter
    Pages 337-339

About this book

Introduction

One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise learning techniques that enable computers to routinely improve their performance through experience, the impact would be enormous. The result would be an explosion of new computer applications that would suddenly become economically feasible (e. g. , personalized computer assistants that automatically tune themselves to the needs of individual users), and a dramatic improvement in the quality of current computer applications (e. g. , imagine an airline scheduling program that improves its scheduling method based on analyzing past delays). And while the potential economic impact ofsuccessful learning methods is sufficient reason to invest in research into machine learning, there is a second significant reason: studying machine learning helps us understand our own human learning abilities and disabilities, leading to the possibility of improved methods in education. While many open questions remain aboutthe methods by which machines and humans might learn, significant progress has been made.

Keywords

Processing algorithms artificial intelligence case-based reasoning cognitive science genetic algorithms intelligence intelligent systems learning logical reasoning machine learning programming

Editors and affiliations

  • Susan Chipman
    • 1
  • Alan L. Meyrowitz
    • 2
  1. 1.Office of Naval ResearchUSA
  2. 2.Naval Research LaboratoryUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-3172-2
  • Copyright Information Kluwer Academic Publishers 1993
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-6390-3
  • Online ISBN 978-1-4615-3172-2
  • Series Print ISSN 0893-3405
  • About this book