Towards Reformulating Essence Specifications for Robustness
This work addresses robustness in automated constraint modeling for users of the Essence language, though it is incremental as it builds on existing refinement techniques.
The paper tackles the problem of Essence specifications being expressed in many equivalent ways, which can limit the applicability of refinement rules and reduce the quality of output constraint models. It presents reformulation rules that automatically recover domain attributes or abstract types, increasing the number and quality of models produced by the Conjure tool.
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.