Changes of problem representation : theory and experiments
Author(s)
Bibliographic Information
Changes of problem representation : theory and experiments
(Studies in fuzziness and soft computing, v. 110)
Physica-Verlag, c2003
Available at 2 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references (p. [343]-355)
Description and Table of Contents
Description
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.
Table of Contents
I. Introduction.- 1. Motivation.- 2. Prodigy search.- II. Description changers.- 3. Primary effects.- 4. Abstraction.- 5. Summary and extensions.- III. Top-level control.- 6. Multiple representations.- 7. Statistical selection.- 8. Statistical extensions.- 9. Summary and extensions.- IV. Empirical results.- 10. Machining Domain.- 11. Sokoban Domain.- 12. Extended Strips Domain.- 13. Logistics Domain.- Concluding remarks.- References.
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