Towards Reformulating Essence Specifications for Robustness

TL;DR

This paper proposes reformulation rules for the Essence language to automatically recover omitted domain attributes and types, enhancing the robustness and diversity of constraint models generated by Conjure.

Contribution

It introduces reformulation rules that modify variable types and attributes to improve model generation robustness in Essence specifications.

Findings

Increased number of models generated from reformulated specifications.

Improved quality of models in terms of constraint diversity.

Enhanced robustness against variations in input specifications.

Abstract

The Essence language allows a user to specify a constraint problem at a level of abstraction above that at which constraint modelling decisions are made. Essence specifications are refined into constraint models using the Conjure automated modelling tool, which employs a suite of refinement rules. However, Essence is a rich language in which there are many equivalent ways to specify a given problem. A user may therefore omit the use of domain attributes or abstract types, resulting in fewer refinement rules being applicable and therefore a reduced set of output models from which to select. This paper addresses the problem of recovering this information automatically to increase the robustness of the quality of the output constraint models in the face of variation in the input Essence specification. We present reformulation rules that can change the type of a decision variable or add attributes that shrink its domain. We demonstrate the efficacy of this approach in terms of the quantity and quality of models Conjure can produce from the transformed specification compared with the original.