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Configurable solvers: Tailoring general methods to specific applications

  • Steven Minton
Invited Talk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1330)

Abstract

Applying constraint-based problem solving methods in a new domain often requires considerable work. In this talk I will examine the state of the art in constraint-based problem solving techniques and the difficulties involved in selecting and tuning an algorithm to solve a problem. Most constraint-based solvers have many algorithmic variations, and it can make a very significant difference exactly which algorithm is used and how the problem is encoded. I will describe promising new approaches in which generic algorithms are automatically configured for specific applications.

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References

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    J. Allen and S. Minton. Selecting the right heuristic algorithm: Runtime performance predictors. In Proceedings of the Canadian AI Conference, 1996.Google Scholar
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    D.J. Cook and R.Craig Varnell. Maximizing the benefits of parallel search using machine learning. In Proceedings AAAI-97, 1997.Google Scholar
  3. 3.
    S. Minton. An analytic learning system for specializing heuristics. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, 1993.Google Scholar
  4. 4.
    S. Minton. Automatically configuring constraint satisfaction programs: A case study (in press. Constraints, 1(1), 1996.Google Scholar
  5. 5.
    D.R. Smith. KIDS: A knowledge-based software development system. In M.R. Lowry and R.D. McCartney, editors, Automating Software Design. AAAI Press, 1991.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Steven Minton
    • 1
  1. 1.USC Information Sciences InstituteMarina del Rey

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