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摘要翻译：
为了建立包含不确定性和概率的组合决策问题的模型，我们将[Walsh,2002]中提出的随机约束规划框架沿着一些重要的维度（如多重机会约束和一系列新目标）进行了扩展。我们还提供了一种新的（但等价的）基于场景的语义。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，我们对金融、农业和生产等领域的广泛问题进行了建模。
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英文标题：
《Scenario-based Stochastic Constraint Programming》
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作者：
Suresh Manandhar, Armagan Tarim, Toby Walsh
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最新提交年份：
2009
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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。
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英文摘要：
To model combinatorial decision problems involving uncertainty and probability, we extend the stochastic constraint programming framework proposed in [Walsh, 2002] along a number of important dimensions (e.g. to multiple chance constraints and to a range of new objectives). We also provide a new (but equivalent) semantics based on scenarios. Using this semantics, we can compile stochastic constraint programs down into conventional (nonstochastic) 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 finance, agriculture and production.
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PDF链接：
https://arxiv.org/pdf/0905.3763
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