It is well established that formulating an effective constraint model of a problem of interest is crucial to the efficiency with which it can subsequently be solved. Following from the observation that it is difficult, if not impossible, to know a priori which of a set of candidate models will perform best in practice, we envisage a system that explores the space of models through a process of reformulation from an initial model, guided by performance on a set of training instances from the problem class under consideration. We plan to situate this system in a refinement-based approach, where a user writes a constraint specification describing a problem above the level of abstraction at which many modelling decisions are made. In this position paper we set out our plan for an exploratory reformulation system, and discuss progress made so far.

翻译：众所周知，对所关注的问题构建有效的约束模型，对于后续求解效率至关重要。基于"预先判断一组候选模型中哪个在实际中表现最佳即便并非不可能，也是极其困难的"这一观察，我们设想了一个系统，该系统通过从初始模型出发的重构过程来探索模型空间，并以所考虑问题类别的训练实例集上的性能表现为引导。我们计划将该系统置于基于精化的方法框架内，用户可在高于诸多建模决策所涉及抽象层次的层面上，编写描述问题的约束规约。在本立场论文中，我们阐述了探索式重构系统的规划方案，并讨论了迄今取得的进展。