摘要翻译：
模糊约束是处理偏好和过度约束问题的一种流行方法，在需要谨慎的场景中，如在医疗或空间应用中。我们在这里考虑一些偏好可能缺失的模糊约束问题。例如，这种模型用于代理分布且存在隐私问题的设置，或者存在正在进行的偏好启发过程的设置。在这种情况下，我们研究如何找到一个最优的解决方案，而不考虑缺失的偏好。在寻找这样一个解决方案的过程中，如果有必要，我们可能会从用户那里引出偏好。然而，我们的目标是尽可能少地询问用户。我们定义了一个联合求解和偏好启发方案，其中包含了大量不同的实例，每个实例对应于一个具体的算法，并进行了实验比较。我们计算被激发的首选项的数量和“用户努力”，这可能更大，因为它包含用户必须计算的所有首选项值，以便能够响应激发请求。当所关注的是尽可能少地传递信息时，所引出的偏好的数量是很重要的，而用户努力也度量了用户为了能够传递所引出的偏好而必须做的隐藏工作。我们的实验结果表明，我们的一些算法很好地找到了一个必要的最优解，而用户只需要很小一部分丢失的偏好。对于最好的算法，用户的努力也非常小。最后，我们在可能存在缺失约束的硬约束问题上测试了这些算法，目标是在不考虑缺失约束的情况下找到可行解。
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英文标题：
《Elicitation strategies for fuzzy constraint problems with missing
  preferences: algorithms and experimental studies》
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作者：
Mirco Gelain, Maria Pini, Francesca Rossi, Brent Venable and 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：
  Fuzzy constraints are a popular approach to handle preferences and over-constrained problems in scenarios where one needs to be cautious, such as in medical or space applications. We consider here fuzzy constraint problems where some of the preferences may be missing. This models, for example, settings where agents are distributed and have privacy issues, or where there is an ongoing preference elicitation process. In this setting, we study how to find a solution which is optimal irrespective of the missing preferences. In the process of finding such a solution, we may elicit preferences from the user if necessary. However, our goal is to ask the user as little as possible. We define a combined solving and preference elicitation scheme with a large number of different instantiations, each corresponding to a concrete algorithm which we compare experimentally. We compute both the number of elicited preferences and the "user effort", which may be larger, as it contains all the preference values the user has to compute to be able to respond to the elicitation requests. While the number of elicited preferences is important when the concern is to communicate as little information as possible, the user effort measures also the hidden work the user has to do to be able to communicate the elicited preferences. Our experimental results show that some of our algorithms are very good at finding a necessarily optimal solution while asking the user for only a very small fraction of the missing preferences. The user effort is also very small for the best algorithms. Finally, we test these algorithms on hard constraint problems with possibly missing constraints, where the aim is to find feasible solutions irrespective of the missing constraints.
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PDF链接：
https://arxiv.org/pdf/0909.4446