摘要翻译：
二元关系框架已被证明适用于许多现实生活中的偏好处理场景。这里我们研究偏好收缩：丢弃选定偏好的问题。我们认为极小性和严格偏序的保持是收缩的关键。可以通过指定应该保护哪些首选项来进一步限制收缩。我们考虑两类偏好关系：有限的和有限可表示的。对于有限和有限可表示的偏好关系，我们给出了计算最小收缩和保护偏好的最小收缩的算法。我们研究了二元关系框架中偏好变化与信念修正理论中信念变化之间的关系。我们还介绍了一些偏好查询优化技术，这些技术可以在存在压缩的情况下使用。我们对所提出的算法进行了实验评估，并给出了结果。
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英文标题：
《Contracting preference relations for database applications》
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作者：
Denis Mindolin, Jan Chomicki
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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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一级分类：Computer Science        计算机科学
二级分类：Databases        数据库
分类描述：Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
涵盖数据库管理、数据挖掘和数据处理。大致包括ACM学科类E.2、E.5、H.0、H.2和J.1中的材料。
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英文摘要：
  The binary relation framework has been shown to be applicable to many real-life preference handling scenarios. Here we study preference contraction: the problem of discarding selected preferences. We argue that the property of minimality and the preservation of strict partial orders are crucial for contractions. Contractions can be further constrained by specifying which preferences should be protected. We consider two classes of preference relations: finite and finitely representable. We present algorithms for computing minimal and preference-protecting minimal contractions for finite as well as finitely representable preference relations. We study relationships between preference change in the binary relation framework and belief change in the belief revision theory. We also introduce some preference query optimization techniques which can be used in the presence of contraction. We evaluate the proposed algorithms experimentally and present the results.
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PDF链接：
https://arxiv.org/pdf/0903.1878