重构全局语法约束
Reformulating Global Grammar Constraints
摘要：
一种有吸引力的机制来指定全局约束在排班和其他领域是通过形式语言。例如，正则和语法约束分别根据自动机和上下文无关文法接受的语言来指定约束。利用约束的固定长度，我们给出了一个将上下文无关文法转换为自动机的算法。然后，我们研究使用最小化技术，以减少这种自动机的大小，加快传播。我们表明，最小化这样的自动机后，他们已经展开和域最初减少可以得到更紧凑的自动机比最小化前展开和减少。实验结果表明，这种转换可以提高我们可以“建模和运行”的排课问题的大小。
英文摘要：
An attractive mechanism to specify global constraints in rostering and other domains is via formal languages. For instance, the Regular and Grammar constraints specify constraints in terms of the languages accepted by an automaton and a context-free grammar respectively. Taking advantage of the fixed length of the constraint, we give an algorithm to transform a context-free grammar into an automaton. We then study the use of minimization techniques to reduce the size of such automata and speed up propagation. We show that minimizing such automata after they have been unfolded and domains initially reduced can give automata that are more compact than minimizing before unfolding and reducing. Experimental results show that such transformations can improve the size of rostering problems that we can 'model and run'.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
