An algorithm for solving the job-shop problem
Management Science
Combinatorial aspects of construction of competition Dutch Professional Football Leagues
Discrete Applied Mathematics - Special issue: Timetabling and chromatic scheduling
Scheduling a Major College Basketball Conference
Operations Research
Scheduling a Major College Basketball Conference--Revisited
Operations Research
Constraint-Based Local Search
Integrated Methods for Optimization (International Series in Operations Research & Management Science)
The Design of the Zinc Modelling Language
Constraints
Open Constraints in a Boundable World
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Automatic Generation of Implied Constraints
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
On Learning Constraint Problems
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 01
A constraint seeker: finding and ranking global constraints from examples
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
A SAT-based version space algorithm for acquiring constraint satisfaction problems
ECML'05 Proceedings of the 16th European conference on Machine Learning
Constraint acquisition via partial queries
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.