摘要翻译：
许多现实生活中的优化问题既包含硬约束，也包含软约束，以及定性的条件偏好。但是，没有单一的形式主义来指定所有这三种信息。因此，我们提出了一个基于CP-nets和软约束的框架，该框架能够高效、统一地处理硬约束和软约束以及条件偏好。我们研究了偏好语句一致性测试的复杂性，并说明了软约束如何在提高计算复杂性的同时忠实地逼近条件偏好语句的语义
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英文标题：
《Reasoning about soft constraints and conditional preferences: complexity
  results and approximation techniques》
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作者：
Carmel Domshlak, Francesca Rossi, Kristen Brent Venable, 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：
  Many real life optimization problems contain both hard and soft constraints, as well as qualitative conditional preferences. However, there is no single formalism to specify all three kinds of information. We therefore propose a framework, based on both CP-nets and soft constraints, that handles both hard and soft constraints as well as conditional preferences efficiently and uniformly. We study the complexity of testing the consistency of preference statements, and show how soft constraints can faithfully approximate the semantics of conditional preference statements whilst improving the computational complexity
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PDF链接：
https://arxiv.org/pdf/0905.3766