摘要翻译：
用户对自动帮助的偏好通常差异很大，这取决于情况、帮助的质量或表示。开发effectivemodels以在线学习个人偏好需要将用户行为的观察与其效用函数联系起来的领域模型，而效用函数又可以使用效用启发技术来构建。然而，大多数启发式方法要求用户基于假设场景的预测效用，而不是更现实的经验效用。在界面定制中尤其如此，用户被要求评估新的界面设计。我们提出了用于定制的经验效用启发方法，并将其与预测方法进行了比较。由于经验丰富的实用程序被认为能更好地反映行为决策中的真实偏好，这里的目的是研究适合于软件领域的准确和有效的过程。与传统的启发式不同，我们的结果表明，经验方法有助于人们理解随机结果，以及更好地理解智能辅助的顺序效用。
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英文标题：
《Toward Experiential Utility Elicitation for Interface Customization》
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作者：
Bowen Hui, Craig Boutilier
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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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一级分类：Computer Science        计算机科学
二级分类：Human-Computer Interaction        人机交互
分类描述：Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
包括人为因素、用户界面和协作计算。大致包括ACM学科课程H.1.2和所有H.5中的材料，除了H.5.1，它更有可能以多媒体作为主要学科领域。
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英文摘要：
  User preferences for automated assistance often vary widely, depending on the situation, and quality or presentation of help. Developing effectivemodels to learn individual preferences online requires domain models that associate observations of user behavior with their utility functions, which in turn can be constructed using utility elicitation techniques. However, most elicitation methods ask for users' predicted utilities based on hypothetical scenarios rather than more realistic experienced utilities. This is especially true in interface customization, where users are asked to assess novel interface designs. We propose experiential utility elicitation methods for customization and compare these to predictivemethods. As experienced utilities have been argued to better reflect true preferences in behavioral decision making, the purpose here is to investigate accurate and efficient procedures that are suitable for software domains. Unlike conventional elicitation, our results indicate that an experiential approach helps people understand stochastic outcomes, as well as better appreciate the sequential utility of intelligent assistance.
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PDF链接：
https://arxiv.org/pdf/1206.3258