Modeling a combinatorial problem is a hard and error-prone task requiring significant expertise. Constraint acquisition methods attempt to automate this process by learning constraints from examples of solutions and (usually) non-solutions. Active methods query an oracle while passive methods do not. We propose a known but not widely-used application of machine learning to constraint acquisition: training a classifier to discriminate between solutions and non-solutions, then deriving a constraint model from the trained classifier. We discuss a wide range of possible new acquisition methods with useful properties inherited from classifiers. We also show the potential of this approach using a Naive Bayes classifier, obtaining a new passive acquisition algorithm that is considerably faster than existing methods, scalable to large constraint sets, and robust under errors.

对组合问题进行建模是一项艰巨且容易出错的任务，需要大量的专业知识。约束获取方法尝试通过从解决方案和（通常）非解决方案示例中学习约束来使此过程自动化。主动方法查询一个oracle，而被动方法不查询。我们提出了一种已知但尚未广泛使用的机器学习应用于约束获取的方法：训练分类器以区分解决方案和非解决方案，然后从经过训练的分类器中得出约束模型。我们讨论了从分类器继承的有用属性的各种可能的新采集方法。我们还展示了使用朴素贝叶斯分类器的这种方法的潜力，获得了一种新的被动采集算法，该算法比现有方法要快得多，可扩展到大约束集，