Extensible Automated Constraint Modelling


  • Ozgur Akgun University of St. Andrews
  • Ian Miguel University of St. Andrews
  • Chris Jefferson University of St. Andrews
  • Alan Frisch University of York
  • Brahim Hnich Izmir University of Economics


In constraint solving, a critical bottleneck is the formulation of aneffective constraint model of an input problem. The Conjure system describedin this paper, a substantial step forward over prototype versions of Conjurepreviously reported, makes a valuable contribution to the automation ofconstraint modelling by automatically producing constraint models from theirspecifications in the abstract constraint specification language Essence. Aset of rules is used to refine an abstract specification into a concreteconstraint model. We demonstrate that this set of rules is readily extensibleto increase the space of possible constraint models Conjure can produce. Ourempirical results confirm that Conjure can reproduce successfully the kernelsof the constraint models of 32 benchmark problems found in the literature.




How to Cite

Akgun, O., Miguel, I., Jefferson, C., Frisch, A., & Hnich, B. (2011). Extensible Automated Constraint Modelling. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 4-11. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7820



Constraints, Satisfiability, and Search