We present a method to detect implicit model patterns (such as global constraints) that might be able to replace parts of a combinatorial problem model that are expressed at a low-level. This can help non-expert users write higher-level models that are easier to reason about and often yield better performance. Our method generates candidate model patterns by analyzing both the structure of the model – its constraints, variables, parameters and loops – and the input data from one or more data files. Each candidate is scored by comparing a sample of its solution space with that of the part of the model it is intended to replace. The top-scoring candidates are presented to the user through an interactive display, which shows how they could be incorporated into the model. The method is implemented for the MiniZinc modeling language and available as part of the MiniZinc distribution.

我们提出了一种检测隐式模型模式（例如全局约束）的方法，该方法可能能够替换在低级别表达的组合问题模型的部分。这可以帮助非专家用户编写更易于推理并且通常会产生更好性能的更高级别的模型。我们的方法通过分析模型的结构——其约束、变量、参数和循环——以及来自一个或多个数据文件的输入数据来生成候选模型模式。通过将其解决方案空间的样本与其要替换的模型部分的样本进行比较，对每个候选对象进行评分。得分最高的候选者通过交互式显示呈现给用户，显示如何将它们合并到模型中。该方法是为MiniZinc建模语言并作为 MiniZinc 发行版的一部分提供。