摘要翻译：
在现实生活的时间场景中，不确定性和偏好往往是基本的和并存的方面。我们提出了一种形式化的方法，其中可以定义具有偏好和不确定性的定量时间约束。我们展示了三个经典的可控性概念（即强、弱和动态）是如何被推广到处理偏好的，这些概念是针对不确定的时间问题发展起来的。在定义了这个通用框架之后，我们重点关注偏好遵循模糊方法的问题，并且具有确保可处理性的属性。针对这类问题，我们提出了算法来检验可控性的存在性。特别地，我们证明了在这样的环境下同时处理偏好和不确定性并不会增加可控性测试的复杂性。我们还开发了一个多项式复杂度的动态执行算法，该算法在不确定情况下产生相对于模糊偏好最优的时间计划。
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英文标题：
《Uncertainty in Soft Temporal Constraint Problems:A General Framework and
  Controllability Algorithms for the Fuzzy Case》
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作者：
F. Rossi, K. B. Venable, N. Yorke-Smith
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最新提交年份：
2011
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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：
  In real-life temporal scenarios, uncertainty and preferences are often essential and coexisting aspects. We present a formalism where quantitative temporal constraints with both preferences and uncertainty can be defined. We show how three classical notions of controllability (that is, strong, weak, and dynamic), which have been developed for uncertain temporal problems, can be generalized to handle preferences as well. After defining this general framework, we focus on problems where preferences follow the fuzzy approach, and with properties that assure tractability. For such problems, we propose algorithms to check the presence of the controllability properties. In particular, we show that in such a setting dealing simultaneously with preferences and uncertainty does not increase the complexity of controllability testing. We also develop a dynamic execution algorithm, of polynomial complexity, that produces temporal plans under uncertainty that are optimal with respect to fuzzy preferences.
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PDF链接：
https://arxiv.org/pdf/1110.2212