ModelSeeker: Extracting Global Constraint Models from Positive Examples - Archive ouverte HAL Access content directly
Book Sections Year : 2016

ModelSeeker: Extracting Global Constraint Models from Positive Examples

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.
No file

Dates and versions

hal-01436062 , version 1 (16-01-2017)

Identifiers

Cite

Nicolas Beldiceanu, Helmut Simonis. ModelSeeker: Extracting Global Constraint Models from Positive Examples. Christian Bessiere; Luc De Raedt; Lars Kotthoff; Siegfried Nijssen; Barry O'Sullivan; Dino Pedreschi. Data Mining and Constraint Programming, 10101, Springer, pp.77-95, 2016, Data Mining and Constraint Programming, 978-3-319-50136-9. ⟨10.1007/978-3-319-50137-6_4⟩. ⟨hal-01436062⟩
222 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More