Constraint Acquisition (CA) aims to widen the use of constraint programming by assisting users in the modeling process. However, most CA methods suffer from a significant drawback: they learn a single set of individual constraints for a specific problem instance, but cannot generalize these constraints to the parameterized constraint specifications of the problem. In this paper, we address this limitation by proposing GenCon, a novel approach to learn parameterized constraint models capable of modeling varying instances of the same problem. To achieve this generalization, we make use of statistical learning techniques at the level of individual constraints. Specifically, we propose to train a classifier to predict, for any possible constraint and parameterization, whether the constraint belongs to the problem. We then show how, for some classes of classifiers, we can extract decision rules to construct interpretable constraint specifications. This enables the generation of ground constraints for any parameter instantiation. Additionally, we present a generate-and-test approach that can be used with any classifier, to generate the ground constraints on the fly. Our empirical results demonstrate that our approach achieves high accuracy and is robust to noise in the input instances.

翻译：约束获取旨在通过辅助用户进行建模过程来扩大约束编程的应用范围。然而，大多数约束获取方法存在一个显著缺陷：它们针对特定问题实例学习一组单一的个体约束，但无法将这些约束泛化到问题的参数化约束规约。在本文中，我们通过提出GenCon这一新方法来解决这一局限性，该方法能够学习参数化约束模型，从而对同一问题的不同实例进行建模。为实现这种泛化，我们在个体约束层面利用了统计学习技术。具体而言，我们提出训练一个分类器，用于预测任意可能的约束及其参数化是否属于该问题。随后我们证明，对于某些分类器类别，可以提取决策规则以构建可解释的约束规约。这使得能够为任何参数实例生成具体约束。此外，我们提出了一种生成-测试方法，该方法可与任何分类器结合使用，以动态生成具体约束。我们的实证结果表明，所提方法实现了高准确率，并且对输入实例中的噪声具有鲁棒性。