The Rules of Constraint Modelling

A.M. Frisch; C. Jefferson; B. Martinez Hernandez; I. Miguel

The Rules of Constraint Modelling

A.M. Frisch, C. Jefferson, B. Martinez Hernandez, I. Miguel

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Many and diverse combinatorial problems have been solved successfully using finite-domain constraint programming. However, to apply constraint programming to a particular domain, the problem must first be modelled as a constraint satisfaction or optimisation problem. Since constraints provide a rich language, typically many alternative models exist. Formulating a good model therefore requires a great deal of expertise. This paper describes CONJURE, a system that refines a specification of a problem in the abstract constraint specification language ESSENCE into a set of alternative constraint models. Refinement is compositional: alternative constraint models are generated by composing refinements of the components of the specification. Experimental results demonstrate that CONJURE is able to generate a variety of models for practical problems from their ESSENCE specifications.

Original language	English
Title of host publication	Proceedings of the 19th International Joint Conference on Artificial Intelligence
Pages	109-116
Number of pages	7
Publication status	Published - 2005
Event	IJCAI-05 - Edinburgh, Scotland Duration: 30 Jul 2005 → 5 Aug 2005

Conference

Conference	IJCAI-05
City	Edinburgh, Scotland
Period	30/07/05 → 5/08/05

Access to Document

http://www.ijcai.org/papers/1667.pdf

Cite this

@inproceedings{0d7cc3c3f0c84de885bab083d22e2a08,

title = "The Rules of Constraint Modelling",

abstract = "Many and diverse combinatorial problems have been solved successfully using finite-domain constraint programming. However, to apply constraint programming to a particular domain, the problem must first be modelled as a constraint satisfaction or optimisation problem. Since constraints provide a rich language, typically many alternative models exist. Formulating a good model therefore requires a great deal of expertise. This paper describes CONJURE, a system that refines a specification of a problem in the abstract constraint specification language ESSENCE into a set of alternative constraint models. Refinement is compositional: alternative constraint models are generated by composing refinements of the components of the specification. Experimental results demonstrate that CONJURE is able to generate a variety of models for practical problems from their ESSENCE specifications.",

author = "A.M. Frisch and C. Jefferson and {Martinez Hernandez}, B. and I. Miguel",

year = "2005",

language = "English",

pages = "109--116",

booktitle = "Proceedings of the 19th International Joint Conference on Artificial Intelligence",

note = "IJCAI-05 ; Conference date: 30-07-2005 Through 05-08-2005",

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T1 - The Rules of Constraint Modelling

AU - Frisch, A.M.

AU - Jefferson, C.

AU - Martinez Hernandez, B.

AU - Miguel, I.

PY - 2005

Y1 - 2005

N2 - Many and diverse combinatorial problems have been solved successfully using finite-domain constraint programming. However, to apply constraint programming to a particular domain, the problem must first be modelled as a constraint satisfaction or optimisation problem. Since constraints provide a rich language, typically many alternative models exist. Formulating a good model therefore requires a great deal of expertise. This paper describes CONJURE, a system that refines a specification of a problem in the abstract constraint specification language ESSENCE into a set of alternative constraint models. Refinement is compositional: alternative constraint models are generated by composing refinements of the components of the specification. Experimental results demonstrate that CONJURE is able to generate a variety of models for practical problems from their ESSENCE specifications.

AB - Many and diverse combinatorial problems have been solved successfully using finite-domain constraint programming. However, to apply constraint programming to a particular domain, the problem must first be modelled as a constraint satisfaction or optimisation problem. Since constraints provide a rich language, typically many alternative models exist. Formulating a good model therefore requires a great deal of expertise. This paper describes CONJURE, a system that refines a specification of a problem in the abstract constraint specification language ESSENCE into a set of alternative constraint models. Refinement is compositional: alternative constraint models are generated by composing refinements of the components of the specification. Experimental results demonstrate that CONJURE is able to generate a variety of models for practical problems from their ESSENCE specifications.

M3 - Conference contribution

SP - 109

EP - 116

BT - Proceedings of the 19th International Joint Conference on Artificial Intelligence

T2 - IJCAI-05

Y2 - 30 July 2005 through 5 August 2005

ER -