随机约束规划
Stochastic Constraint Programming
摘要：
为了模拟涉及不确定性和概率的组合决策问题，我们引入了随机约束规划。随机约束程序包含决策变量（我们可以设置）和随机变量（遵循概率分布）。它们结合了传统的约束满足，随机整数规划和随机可满足性的最佳功能。我们给出了随机约束规划的一个语义，并提出了一些完整的算法和近似程序。最后，我们讨论了一些扩展的随机约束规划放松各种假设，如随机变量之间的独立性，并比较与其他方法的决策下的不确定性。
英文摘要：
To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision variables (which we can set) and stochastic variables (which follow a probability distribution). They combine together the best features of traditional constraint satisfaction, stochastic integer programming, and stochastic satisfiability. We give a semantics for stochastic constraint programs, and propose a number of complete algorithms and approximation procedures. Finally, we discuss a number of extensions of stochastic constraint programming to relax various assumptions like the independence between stochastic variables, and compare with other approaches for decision making under uncertainty.
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。
