Abstraction, Reformulation, and Approximation

1. Front Matter

   Pages I-XI

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2. Invited Talks

   1. Partial Completeness of Abstract Fixpoint Checking

      • Patrick Cousot

      Pages 1-25

   2. An Overview of MAXQ Hierarchical Reinforcement Learning

      • Thomas G. Dietterich

      Pages 26-44

   3. Recent Progress in the Design and Analysis of Admissible Heuristic Functions

      • Richard E. Korf

      Pages 45-55

3. Tutorial

   1. GIS Databases: From Multiscale to MultiRepresentation

      • Stefano Spaccapietra, Christine Parent, Christelle Vangenot

      Pages 57-70

4. Full Papers

   1. An Abstraction Framework for Soft Constraints and Its Relationship with Constraint Propagation

      • Stefano Bistarelli, Philippe Codognet, Francesca Rossi

      Pages 71-86

   2. Abstractions for Knowledge Organization of Relational Descriptions

      • Isabelle Bournaud, Mélanie Courtine, Jean-Daniel Zucker

      Pages 87-106

   3. Grid-Based Histogram Arithmetic for the Probabilistic Analysis of Functions

      • Carlos Carreras, Manuel V. Hermenegildo

      Pages 107-123

   4. Approximating Data in Constraint Databases

      • Rui Chen, Min Ouyang, Peter Z. Revesz

      Pages 124-143

   5. Linearly Bounded Reformulations of Unary Databases

      • Rada Chirkova, Michael R. Genesereth

      Pages 144-163

   6. A CSP Abstraction Framework

      • Christophe Lecoutre, Sylvain Merchez, Frédéric Boussemart, Eric Grégoire

      Pages 164-184

   7. Interactions of Abstractions in Programming

      • Gordon S. Novak Jr.

      Pages 185-201

   8. Reformulation and Approximation in Model Checking

      • Peter Z. Revesz

      Pages 202-218

   9. The Lumberjack Algorithm for Learning Linked Decision Forests

      • William T. B. Uther, Manuela M. Veloso

      Pages 219-232

   10. Reformulating Propositional Satisfiability as Constraint Satisfaction

       • Toby Walsh

       Pages 233-246

5. Extended Abstracts

   1. Improving the Efficiency of Reasoning Through Structure-Based Reformulation

      • Eyal Amir, Sheila McIlraith

      Pages 247-259

   2. Using Feature Hierarchies in Bayesian Network Learning

      • Marie desJardins, Lise Getoor, Daphne Koller

      Pages 260-270

   3. On Reformulating Planning as Dynamic Constraint Satisfaction

      • Jeremy Frank, Ari K. Jónsson, Paul Morris

      Pages 271-280

   4. Experiments with Automatically Created Memory-Based Heuristics

      • István T. Hernádvölgyi, Robert C. Holte

      Pages 281-290

   5. Abstraction and Phase Transitions in Relational Learning

      • Lorenza Saitta, Jean-Daniel Zucker

      Pages 291-301

This volume contains the proceedings of SARA 2000, the fourth Symposium on Abstraction, Reformulations, and Approximation (SARA). The conference was held at Horseshoe Bay Resort and Conference Club, Lake LBJ, Texas, July 26– 29, 2000, just prior to the AAAI 2000 conference in Austin. Previous SARA conferences took place at Jackson Hole in Wyoming (1994), Ville d’Est´erel in Qu´ebec (1995), and Asilomar in California (1998). The symposium grewout of a series of workshops on abstraction, approximation, and reformulation that had taken place alongside AAAI since 1989. This year’s symposium was actually scheduled to take place at Lago Vista Clubs & Resort on Lake Travis but, due to the resort’s failure to pay taxes, the conference had to be moved late in the day. This mischance engendered eleventh-hour reformulations, abstractions, and resource re-allocations of its own. Such are the perils of organizing a conference. This is the ?rst SARA for which the proceedings have been published in the LNAI series of Springer-Verlag. We hope that this is a re?ection of the increased maturity of the ?eld and that the increased visibility brought by the publication of this volume will help the discipline grow even further. Abstractions, reformulations, and approximations (AR&A) have found - plications in a variety of disciplines and problems including automatic progr- ming, constraint satisfaction, design, diagnosis, machine learning, planning, qu- itative reasoning, scheduling, resource allocation, and theorem proving. The - pers in this volume capture a cross-section of these application domains.

• Automat
• Constraint Satisfaction
• learning
• logic
• programming
• proving
• theorem proving

• Department of Computer Science and Engineering, University of Nebraska - Lincoln, Lincoln

  Berthe Y. Choueiry

• Department of Computer Science, University of York, Heslington, UK

  Toby Walsh

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