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Changes of Problem Representation

Theory and Experiments

  • Book
  • © 2002

Overview

Authors:
  1. Eugene Fink

    1. Computer Science and Engineering, University of South Florida, Tampa, USA

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

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Table of contents (14 chapters)

  1. Front Matter

    Pages I-XIII
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. Motivation

      • Eugene Fink
      Pages 3-38

    3. Prodigy search

      • Eugene Fink
      Pages 39-78

  3. Description changers

    1. Front Matter

      Pages 79-79
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    2. Primary effects

      • Eugene Fink
      Pages 81-131

    3. Abstraction

      • Eugene Fink
      Pages 133-166

    4. Summary and extensions

      • Eugene Fink
      Pages 167-190

  4. Top-level control

    1. Front Matter

      Pages 191-191
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    2. Multiple representations

      • Eugene Fink
      Pages 193-204

    3. Statistical selection

      • Eugene Fink
      Pages 205-230

    4. Statistical extensions

      • Eugene Fink
      Pages 231-244

    5. Summary and extensions

      • Eugene Fink
      Pages 245-254

  5. Empirical results

    1. Front Matter

      Pages 255-257
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    2. Machining Domain

      • Eugene Fink
      Pages 259-278

    3. Sokoban Domain

      • Eugene Fink
      Pages 279-297

    4. Extended Strips Domain

      • Eugene Fink
      Pages 299-319

    5. Logistics Domain

      • Eugene Fink
      Pages 321-338

  6. Concluding remarks

    1. Concluding remarks

      • Eugene Fink
      Pages 339-341

  7. Back Matter

    Pages 343-357
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Keywords

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.

Authors and Affiliations

  • Computer Science and Engineering, University of South Florida, Tampa, USA

    Eugene Fink

Bibliographic Information

  • Book Title: Changes of Problem Representation

  • Book Subtitle: Theory and Experiments

  • Authors: Eugene Fink

  • Series Title: Studies in Fuzziness and Soft Computing

  • DOI: https://doi.org/10.1007/978-3-7908-1774-4

  • Publisher: Physica Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2002

  • Hardcover ISBN: 978-3-7908-1523-8Published: 22 October 2002

  • Softcover ISBN: 978-3-7908-2518-3Published: 21 October 2010

  • eBook ISBN: 978-3-7908-1774-4Published: 20 March 2013

  • Series ISSN: 1434-9922

  • Series E-ISSN: 1860-0808

  • Edition Number: 1

  • Number of Pages: XIII, 358

  • Topics: Artificial Intelligence, Cognitive Psychology

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