偏好缺失模糊约束问题的启发策略：算法与实验研究
Elicitation strategies for fuzzy constraint problems with missing preferences: algorithms and experimental studies
摘要：
模糊约束是一种流行的方法，可以在需要谨慎的场景中处理偏好和过度约束问题，例如在医疗或太空应用中。我们在这里考虑模糊约束问题的一些偏好可能会丢失。例如，这种模型可以模拟代理分布并具有隐私问题的设置，或者存在持续偏好获取过程的设置。在这种情况下，我们研究如何找到一个解决方案，这是最佳的，而不管失踪的偏好。在寻找这样的解决方案的过程中，我们可能会在必要时从用户那里获得偏好。但是，我们的目标是尽可能少地询问用户。我们定义了一个组合的解决方案和偏好诱导计划，大量不同的实例，每个对应于一个具体的算法，我们比较实验。我们计算引发的偏好和“用户努力”的数量，这可能更大，因为它包含了用户必须计算的所有偏好值，以便能够响应引发请求。虽然当关注的是传达尽可能少的信息时，引出的偏好的数量是重要的，但是用户努力也测量用户为了能够传达引出的偏好而必须做的隐藏工作。我们的实验结果表明，我们的一些算法是非常好的，在找到一个必要的最佳解决方案，同时要求用户只有很小的一部分缺失的偏好。对于最好的算法，用户的努力也非常小。最后，我们测试这些算法的硬约束问题可能丢失的约束，其目的是找到可行的解决方案，而不管丢失的约束。
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.
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。
