Constraint Programming for Probabilistic Reasoning and Compiler Optimization

约束编程是解决困难问题的强大框架。在约束规划方法中，通过对可接受的解决方案陈述约束来建模问题。一旦模型被公式化，各种算法-通常基于搜索-可用于求解模型。有许多有趣的任务，这种方法特别适合，包括排序，调度和规划。这些任务的共同点是，约束是问题的自然组成部分。一个很好的例子是安排人或机器。什么是现成的是限制，如工人只能在一周的某些部分或只能做某些工作。 我提出的研究项目，既有助于约束编程的基础和约束编程的应用。我考虑的应用程序包括用于手持设备的软件，其主要目标是更快的性能和更低的功耗。通过考虑重要的应用来集中研究有几个好处。首先，它使我们能够为应用领域本身做出贡献。其次，它使我们能够识别-并随后克服-现有的约束编程方法的缺点，从而有助于约束编程的基础。最后，它将约束规划的实践扩展到新的领域。

Constraint programming is a powerful framework for solving difficult problems. In the constraint programming methodology, one models a problem by stating constraints on acceptable solutions. Once a model has been formulated, various algorithms---usually based on search---are available for solving the model. There are many interesting tasks for which this approach is particularly suited, including sequencing, scheduling, and planning. What these tasks have in common is that constraints are a natural part of the problem. A good example is scheduling people or machines. What is readily available are constraints such as a worker is only available for certain parts of the week or only able to do certain jobs. I propose research projects that both contribute to the foundations of constraint programming and to applications of constraint programming. The applications that I consider include software for handheld devices, where the primary goals are faster performance and lower power consumption. Focusing the research by considering important applications has several benefits. First, it allows us to contribute to the application areas themselves. Second, it allows us to identify---and subsequently overcome---shortcomings in existing constraint programming approaches and so contribute to the foundations of constraint programming. Finally, it extends the practice of constraint programming to new areas.