Advertisement

Abstraction, Reformulation, and Approximation

5th International Symposium, SARA 2002 Kananaskis, Alberta, Canada August 2–4, 2002 Proceedings

  • Sven Koenig
  • Robert C. Holte

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2371)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2371)

Table of contents

  1. Front Matter
    Pages I-XI
  2. Invited Presentations

    1. Robert P. Kurshan
      Pages 1-17
    2. Derek Long, Maria Fox, Muna Hamdi
      Pages 18-32
  3. Invited Presentations

    1. Tristan Cazenave
      Pages 52-63
    2. Berthe Y. Choueiry, Amy M. Davis
      Pages 64-82
    3. Eric Hansen, Rong Zhou, Zhengzhu Feng
      Pages 83-98
    4. Lina Khatib, Paul Morris, Robert Morris
      Pages 116-125
    5. Daniel Miranker, Malcolm C. Taylor, Anand Padmanaban
      Pages 140-151
    6. Supratik Mukhopadhyay, Andreas Podelski
      Pages 152-169
    7. Balaraman Ravindran, Andrew G. Barto
      Pages 196-211
    8. Martin Stolle, Doina Precup
      Pages 212-223
    9. Xuan-Ha Vu, Djamila Sam-Haroud, Marius-Calin Silaghi
      Pages 224-241
    10. Jean-Daniel Zucker, Nicolas Bredeche, Lorenza Saitta
      Pages 256-273
  4. Short Presentations

    1. Bruno Apolloni, Fabio Baraghini, Giorgio Palmas
      Pages 274-281
    2. J. Christopher Beck, Patrick Prosser, Evgeny Selensky
      Pages 282-289
    3. T. K. Satish Kumar, Richard Dearden
      Pages 290-298
    4. Ilya Levner, 1Vadim Bulitko, Omid Madani, Russell Greiner
      Pages 299-307
    5. William T. B. Uther, Manuela M. Veloso
      Pages 308-315
    6. Jun Zhang, Adrian Silvescu, Vasant Honavar
      Pages 316-323
  5. Research Summaries

  6. Back Matter
    Pages 349-349

About these proceedings

Introduction

It has been recognized since the inception of Artificial Intelligence (AI) that abstractions, problem reformulations, and approximations (AR&A) are central to human common sense reasoning and problem solving and to the ability of systems to reason effectively in complex domains. AR&A techniques have been used to solve a variety of tasks, including automatic programming, constraint satisfaction, design, diagnosis, machine learning, search, planning, reasoning, game playing, scheduling, and theorem proving. The primary purpose of AR&A techniques in such settings is to overcome computational intractability. In addition, AR&A techniques are useful for accelerating learning and for summarizing sets of solutions. This volume contains the proceedings of SARA 2002, the fifth Symposium on Abstraction, Reformulation, and Approximation, held at Kananaskis Mountain Lodge, Kananaskis Village, Alberta (Canada), August 2 4, 2002. The SARA series is the continuation of two separate threads of workshops: AAAI workshops in 1990 and 1992, and an ad hoc series beginning with the "Knowledge Compilation" workshop in 1986 and the "Change of Representation and Inductive Bias" workshop in 1988 with followup workshops in 1990 and 1992. The two workshop series merged in 1994 to form the first SARA. Subsequent SARAs were held in 1995, 1998, and 2000.

Keywords

Automat Constraint Satisfaction learning machine learning problem solving proving theorem proving

Editors and affiliations

  • Sven Koenig
    • 1
  • Robert C. Holte
    • 2
  1. 1.College of ComputingGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of Computing ScienceUniverstity of AlbertaEdmontonCanada

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-45622-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2002
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-43941-7
  • Online ISBN 978-3-540-45622-3
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site
Industry Sectors
Pharma
Automotive
Chemical Manufacturing
Biotechnology
Finance, Business & Banking
Electronics
IT & Software
Telecommunications
Energy, Utilities & Environment
Aerospace
Engineering