Decision-Theoretic Methods for Personalized Adaptive Information Selection and Display

随着信息技术的进步，可供用户同时访问的信息源的数量和规模不断增长，大大增加了各级决策者的信息处理负担。动态地将用户的注意力集中到在给定情况和时间内“最有价值”的可管理的信息子集上，并正确地显示这些信息，可能会对用户做出的决策质量产生关键影响。为了解决这一具有挑战性的问题，我们需要使系统意识到并响应用户的一般和当前的需求和偏好。在这个项目中，PI将开发新的偏好激发和推理技术，用于大规模信息集成和决策支持系统，提供面向用户的选择、转换和集成来自多个异构动态变化信息源的信息。目标是支持每个用户的个性化信息需求，同时避免信息过载，同时保持对用户不断变化的兴趣和上下文的响应，以及近乎实时地对可用数据源的响应。为此，PI将利用并加强偏好表示的图形定性模型、约束优化算法、知识编译策略以及信息处理和集成的决策理论框架的最新发展。他将以他的团队在决策理论人工智能、人们决策策略的建模和推理以及人机交互等领域的广泛工作为基础。新技术的有效性将在野外生物学家的交互式应用中得到证明。更广泛的影响：在本研究中开发的新颖的基于偏好的表示技术将在短期内显著地帮助领域生物学家——他们在该领域收集大量异质信息，但处理这些信息的方法有限——为他们提供更丰富的信息捕获和理解，而不会过载。从长远来看，这些相同的用户界面技术也将在其他科学学科中具有价值。此外，它们在各种应用程序中都很有价值，例如帮助管理人员实时监视有关其组织的信息，或者通过向团队成员提供对其当前上下文、角色和团队状态敏感的信息来帮助救援团队做出时间关键的决策。更一般地说，这项工作将帮助人们有效地利用丰富的信息，而不会淹没在其中。

The number and size of simultaneously accessible information sources available to users continuously grows with advances in information technology, dramatically increasing the information processing burden on decision makers at all levels. Dynamically focusing user attention to a manageable subset of information that is "most valuable" at a given situation and time, and properly displaying this information, could have a crucial impact on the quality of the decisions s/he makes. To address this challenging problem, we need to make systems aware of and responsive to the general and current needs and preferences of the user. In this project, the PI will develop new preference elicitation and reasoning technology for use in large-scale information integration and decision support systems that provide user-oriented selection, transformation, and integration of information arriving from multiple, heterogeneous, dynamically changing information sources. The goal is to support each user's personalized information needs while avoiding information overload yet remaining responsive to the user's changing interests and context, and to the available data sources in near real time. To this end, the PI will exploit and enhance recent developments in graphical qualitative models for preference representation, algorithms for constrained optimization, knowledge compilation strategies, and decision-theoretic frameworks for information processing and integration. He will build on his team's extensive prior work in the areas of decision-theoretic artificial intelligence, modeling and reasoning about people's decision strategies, and human-computer interaction. Effectiveness of the new technology will be demonstrated in an interactive application for field biologists.Broader Impacts: The novel preference-based presentation techniques to be developed in this research will in the short run significantly aid field biologists-who gather large quantities of heterogeneous information while in the field but have limited methods for working with this information-by providing them with richer capture and comprehension of information without overload. In the longer run, these same user interface techniques will be of value across other scientific disciplines as well. Furthermore, they will be of value in diverse applications such as helping managers monitor information about their organization in real time, or helping rescue teams make time-critical decisions by providing team members with information that is sensitive to their current context, their role, and the team's state. More generally, this work will help people effectively utilize the abundant information available to them without drowning in it.