Formulating an effective constraint model of a parameterised problem class is crucial to the efficiency with which instances of the class can subsequently be solved. It is difficult to know beforehand which of a set of candidate models will perform best in practice. This paper presents a system that employs graph rewriting to reformulate an input model for improved performance automatically. By situating our work in the Essence abstract constraint specification language, we can use the structure in its high level variable types to trigger rewrites directly. We implement our system via rewrite rules expressed in the Graph Programs 2 language, applied to the abstract syntax tree of an input specification. We show how to automatically translate the solution of the reformulated problem into a solution of the original problem for verification and presentation. We demonstrate the efficacy of our system with a detailed case study.

本研究解决了在参数化问题类中制定有效约束模型的困难，通过图重写的方式自动重构输入模型以提升性能。我们展示了该系统如何利用Essence语言中的高层变量类型结构触发重写，并通过详细的案例研究证明了其有效性，最终为原问题的解决提供了验证和展示。

通过图重写自动化提炼规范的重构