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A Model Seeker: Extracting Global Constraint Models From Positive Examples

Helmut Simonis 1 Nicolas Beldiceanu 2, *
* Corresponding author
2 TASC - Theory, Algorithms and Systems for Constraints
Inria Rennes – Bretagne Atlantique , Département informatique - EMN, LINA - Laboratoire d'Informatique de Nantes Atlantique
Abstract : We describe a system which generates finite domain constraint models from positive example solutions, for highly structured problems. The system is based on the global constraint catalog, providing the library of constraints that can be used in modeling, and the Constraint Seeker tool, which finds a ranked list of matching constraints given one or more sample call patterns. We have tested the modeler with 230 examples, ranging from 4 to 6,500 variables, using between 1 and 7,000 samples. These examples come from a variety of domains, including puzzles, sports-scheduling, packing & placement, and design theory. When comparing against manually specified "canonical" models for the examples, we achieve a hit rate of 50\%, processing the complete benchmark set in less than one hour on a laptop. Surprisingly, in many cases the system finds usable candidate lists even when working with a single, positive example.
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Submitted on : Tuesday, November 20, 2012 - 8:56:08 AM
Last modification on : Friday, July 10, 2020 - 8:10:04 AM


  • HAL Id : hal-00754341, version 1


Helmut Simonis, Nicolas Beldiceanu. A Model Seeker: Extracting Global Constraint Models From Positive Examples. First workshop on COmbining COnstraint solving with MIning and LEarning (CoCoMile'12), Aug 2012, Montpellier, France. ⟨hal-00754341⟩



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