Constraint Programming (CP) is a powerful technique for solving large-scale combinatorial (optimisation) problems. Constraint solving a given problem proceeds in two phases: modelling and solving. Effective modelling has an huge impact on the performance of the solving process. This thesis presents a framework in which the users are not required to make modelling decisions, concrete CP models are automatically generated from a high level problem specification. In this framework, modelling decisions are encoded as generic rewrite rules applicable to many different problems.First, modelling decisions are divided into two broad categories. This categorisation guides the automation of each kind of modelling decision and also leads us to the architecture of the automated modelling tool.Second, a domain-specific declarative rewrite rule language is introduced. Thanks to the rule language, automated modelling transformations and the core system are decoupled. The rule language greatly increases the extensibility and maintainability of the rewrite rules database. The database of rules represents the modelling knowledge acquired after analysis of expert models. This database must be easily extensible to best benefit from the active research on constraint modelling.Third, the automated modelling system Conjure is implemented as a realisation of these ideas; having an implementation enables empirical testing of the quality of generated models. The ease with which rewrite rules can be encoded to produce good models is shown. Furthermore, thanks to the generality of the system, one needs to add a very small number of rules to encode many transformations.Finally, the work is evaluated by comparing the generated models to expert models found in the literature for a wide variety of benchmark problems. This evaluation confirms the hypothesis that expert models can be automatically generated starting from high level problem specifications. A method of automatically identifying good models is also presented.In summary, this thesis presents a framework to enable the automatic generation of efficient constraint models from problem specifications. It provides a pleasant environment for both problem owners and modelling experts. Problem owners are presented with a fully automated constraint solution process, once they have a precise description of their problem. Modelling experts can now encode their precious modelling expertise as rewrite rules instead of merely modelling a single problem; resulting in reusable constraint modelling knowledge.

中文翻译：

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通过完善抽象问题规范可扩展的自动约束建模

约束编程（CP）是解决大规模组合（优化）问题的强大技术。解决给定问题的约束分为两个阶段：建模和求解。有效的建模对求解过程的性能具有巨大影响。本文提出了一个框架，在该框架中，不需要用户做出建模决策，而是根据高层问题规范自动生成具体的CP模型。在此框架中，建模决策被编码为适用于许多不同问题的通用重写规则。首先，建模决策分为两大类。这种分类不仅可以指导各种建模决策的自动化，还可以指导我们建立自动化建模工具的体系结构。其次，引入了特定于域的声明性重写规则语言。由于使用了规则语言，自动建模转换和核心系统得以分离。规则语言极大地提高了重写规则数据库的可扩展性和可维护性。规则数据库表示在分析专家模型后获得的建模知识。该数据库必须易于扩展，以从约束建模的积极研究中获得最大的收益。第三，自动化建模系统Conjure被实现为这些思想的实现。具有实现可以对生成的模型的质量进行经验测试。显示了可以轻松编写重写规则以生成良好模型的过程。此外，由于系统的通用性，最后，需要对生成的模型与文献中针对各种基准问题的专家模型进行比较，从而对工作进行评估。该评估证实了这样的假设：专家模型可以从高级问题规范开始自动生成。综上所述，本文提出了一种框架，能够根据问题说明自动生成有效的约束模型。它为问题所有者和建模专家提供了一个愉快的环境。一旦对问题负责人有准确的描述，他们就会获得一个全自动的约束解决方案。建模专家现在可以将其宝贵的建模专业知识编码为重写规则，而不仅仅是对单个问题进行建模。产生可重用的约束建模知识。