发帖
雷达卡
摘要翻译：
为了对包含不确定性和概率的组合决策问题进行建模，我们引入了基于情景的随机约束规划。随机约束程序既包含我们可以设置的决策变量，也包含遵循离散概率分布的随机变量。我们给出了一种基于场景树的随机约束程序的语义。利用这个语义，我们可以将随机约束程序编译成常规（非随机）约束程序。这使我们能够充分利用现有约束求解器的强大功能。我们在基于OPL约束建模语言[Hentenryck et al.，1999]的随机OPL语言中实现了不确定情况下的决策框架。为了说明这一框架的潜力，我们对投资组合多样化、农业规划和生产/库存管理等领域的一系列问题进行了建模。
---
英文标题：
《Stochastic Constraint Programming: A Scenario-Based Approach》
---
作者：
S. Armagan Tarim and Suresh Manandhar and Toby Walsh
---
最新提交年份：
2009
---
分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
---
英文摘要：
To model combinatorial decision problems involving uncertainty and probability, we introduce scenario based stochastic constraint programming. Stochastic constraint programs contain both decision variables, which we can set, and stochastic variables, which follow a discrete probability distribution. We provide a semantics for stochastic constraint programs based on scenario trees. Using this semantics, we can compile stochastic constraint programs down into conventional (non-stochastic) constraint programs. This allows us to exploit the full power of existing constraint solvers. We have implemented this framework for decision making under uncertainty in stochastic OPL, a language which is based on the OPL constraint modelling language [Hentenryck et al., 1999]. To illustrate the potential of this framework, we model a wide range of problems in areas as diverse as portfolio diversification, agricultural planning and production/inventory management.
---
PDF链接：
https://arxiv.org/pdf/0903.1150
京公网安备 11010802022788号
