Changes of Problem Representation

Theory and Experiments

  • Eugene Fink

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 110)

Table of contents

  1. Front Matter
    Pages I-XIII
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Eugene Fink
      Pages 3-38
    3. Eugene Fink
      Pages 39-78
  3. Description changers

    1. Front Matter
      Pages 79-79
    2. Eugene Fink
      Pages 81-131
    3. Eugene Fink
      Pages 133-166
    4. Eugene Fink
      Pages 167-190
  4. Top-level control

    1. Front Matter
      Pages 191-191
    2. Eugene Fink
      Pages 193-204
    3. Eugene Fink
      Pages 205-230
    4. Eugene Fink
      Pages 231-244
    5. Eugene Fink
      Pages 245-254
  5. Empirical results

    1. Front Matter
      Pages 255-257
    2. Eugene Fink
      Pages 259-278
    3. Eugene Fink
      Pages 279-297
    4. Eugene Fink
      Pages 299-319
    5. Eugene Fink
      Pages 321-338
  6. Concluding remarks

    1. Eugene Fink
      Pages 339-341
  7. Back Matter
    Pages 343-357

About this book


The purpose of our research is to enhance the efficiency of AI problem solvers by automating representation changes. We have developed a system that improves the description of input problems and selects an appropriate search algorithm for each given problem. Motivation. Researchers have accumulated much evidence on the impor­ tance of appropriate representations for the efficiency of AI systems. The same problem may be easy or difficult, depending on the way we describe it and on the search algorithm we use. Previous work on the automatic im­ provement of problem descriptions has mostly been limited to the design of individual learning algorithms. The user has traditionally been responsible for the choice of algorithms appropriate for a given problem. We present a system that integrates multiple description-changing and problem-solving algorithms. The purpose of the reported work is to formalize the concept of representation and to confirm the following hypothesis: An effective representation-changing system can be built from three parts: • a library of problem-solving algorithms; • a library of algorithms that improve problem descriptions; • a control module that selects algorithms for each given problem.


Extension Performance STRIPS algorithm algorithms artificial intelligence artificial intelligence system intelligence learning

Authors and affiliations

  • Eugene Fink
    • 1
  1. 1.Computer Science and EngineeringUniversity of South FloridaTampaUSA

Bibliographic information

  • DOI
  • Copyright Information Physica-Verlag Heidelberg 2002
  • Publisher Name Physica, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-7908-2518-3
  • Online ISBN 978-3-7908-1774-4
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site