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摘要翻译：
在帮助用户发现给定数据库中最想要的项目的系统中，我们考虑了偏好启发式的挑战。过去关于偏好提取的工作集中在提供用户偏好的因数表示的结构化模型上。这类模型需要较少的信息来构造和支持高效的推理算法。本文在这方面有两个重要贡献：（1）因子值函数的强表示定理。（2）一种利用我们的表示结果来解决最优项目选择问题的方法。
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英文标题：
《Compact Value-Function Representations for Qualitative Preferences》
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作者：
Ronen I. Brafman, Carmel Domshlak, Tanya Kogan
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最新提交年份：
2012
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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：
We consider the challenge of preference elicitation in systems that help users discover the most desirable item(s) within a given database. Past work on preference elicitation focused on structured models that provide a factored representation of users' preferences. Such models require less information to construct and support efficient reasoning algorithms. This paper makes two substantial contributions to this area: (1) Strong representation theorems for factored value functions. (2) A methodology that utilizes our representation results to address the problem of optimal item selection.
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PDF链接：
https://arxiv.org/pdf/1207.4126
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