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Change of Representation and Inductive Bias

  • D. Paul Benjamin

Table of contents

  1. Front Matter
    Pages i-xii
  2. Josh D. Tenenberg
    Pages 67-79
  3. Craig A. Knoblock
    Pages 81-104
  4. Patricia J. Riddle
    Pages 105-123
  5. Paul Benjamin, Leo Dorst, Indur Mandhyan, Madeleine Rosar
    Pages 125-146
  6. Devika Subramanian
    Pages 147-167
  7. Robert M. Zimmer
    Pages 169-182
  8. Ranan B. Banerji
    Pages 183-191
  9. Haym Hirsh
    Pages 209-221
  10. Wlodek Zadrozny, Mieczyslaw M. Kokar
    Pages 247-266
  11. Stuart J. Russell, Benjamin N. Grosof
    Pages 267-308
  12. Mieczyslaw M. Kokar
    Pages 309-325
  13. Larry Rendell
    Pages 327-353
  14. Back Matter
    Pages 355-356

About this book

Introduction

Change of Representation and Inductive Bias One of the most important emerging concerns of machine learning researchers is the dependence of their learning programs on the underlying representations, especially on the languages used to describe hypotheses. The effectiveness of learning algorithms is very sensitive to this choice of language; choosing too large a language permits too many possible hypotheses for a program to consider, precluding effective learning, but choosing too small a language can prohibit a program from being able to find acceptable hypotheses. This dependence is not just a pitfall, however; it is also an opportunity. The work of Saul Amarel over the past two decades has demonstrated the effectiveness of representational shift as a problem-solving technique. An increasing number of machine learning researchers are building programs that learn to alter their language to improve their effectiveness. At the Fourth Machine Learning Workshop held in June, 1987, at the University of California at Irvine, it became clear that the both the machine learning community and the number of topics it addresses had grown so large that the representation issue could not be discussed in sufficient depth. A number of attendees were particularly interested in the related topics of constructive induction, problem reformulation, representation selection, and multiple levels of abstraction. Rob Holte, Larry Rendell, and I decided to hold a workshop in 1988 to discuss these topics. To keep this workshop small, we decided that participation be by invitation only.

Keywords

autonomous system hierarchical planning knowledge learning machine learning problem solving proving

Editors and affiliations

  • D. Paul Benjamin
    • 1
  1. 1.Philips LaboratoriesBriarcliff ManorUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4613-1523-0
  • Copyright Information Springer-Verlag US 1990
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8817-6
  • Online ISBN 978-1-4613-1523-0
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site
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