Chris Jefferson

AAAI Conference 2011 Conference Paper

Extensible Automated Constraint Modelling

• Ozgur Akgun
• Ian Miguel
• Chris Jefferson
• Alan Frisch
• Brahim Hnich

In constraint solving, a critical bottleneck is the formulation of an effective constraint model of a given problem. The CONJURE system described in this paper, a substantial step forward over prototype versions of CONJURE previously reported, makes a valuable contribution to the automation of constraint modelling by automatically producing constraint models from their specifications in the abstract constraint specification language ESSENCE. A set of rules is used to re- fine an abstract specification into a concrete constraint model. We demonstrate that this set of rules is readily extensible to increase the space of possible constraint models CONJURE can produce. Our empirical results confirm that CONJURE can reproduce successfully the kernels of the constraint models of 32 benchmark problems found in the literature.

IJCAI Conference 2007 Conference Paper

• Alan M. Frisch
• Matthew Grum
• Chris Jefferson
• Bernadette Mart
• iacute; nez Hern
• aacute; ndez
• Ian Miguel

ESSENCE is a new formal language for specifying combinatorial problems in a manner similar to natural rigorous specifications that use a mixture of natural language and discrete mathematics. ESSENCE provides a high level of abstraction, much of which is the consequence of the provision of decision variables whose values can be combinatorial objects, such as tuples, sets, multisets, relations, partitions and functions. ESSENCE also allows these combinatorial objects to be nested to arbitrary depth, thus providing, for example, sets of partitions, sets of sets of partitions, and so forth. Therefore, a problem that requires finding a complex combinatorial object can be directly specified by using a decision variable whose type is precisely that combinatorial object.

IJCAI Conference 2005 Conference Paper

The Rules of Constraint Modelling

• Alan M. Frisch
• Chris Jefferson
• Bernadette Martínez-Hernández
• Ian

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.