EAGER: Preferences in Repeated Choices

EAGER：重复选择中的偏好

• 批准号: 1649152
• 负责人: Judith Goldsmith
• 金额: $ 7万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1649152&HistoricalAwards=false
• 关键词: EAGER; Preferences; Repeated; Choices

Computational models of preferences are important for recommendation and decision-support systems, with applications including long-term planners, smart homes, and health-care monitors. Because there is a trade-off between the expressivity of preference representations and the ease of reasoning with them, most personalized preference-based systems rely on either statistical models of similar individuals, or very simple models of one individual?s preferences. We propose to work with conditional preference models, which can represent more complex preferences. Our work will be to develop better algorithms for reasoning about preferences over complex outcomes or scenarios, given such models, and also to extend the model to be able to reason about repeated choices over time. In addition, the PI will use preference modeling software in her outreach to schools, colleges, and the general public to show ways that computers can model and reason about preferences. The PI has a record of supporting a diverse group of students, including students from the LGBTQA community, students with learning disabilities, women, and people of color. Thus, the broader impacts include outreach and a software package, which will be publicly available, for outreach about AI and preference handling, contributions to the infrastructure of preference reasoning research, and support of diversity in computer science. The first part of our proposal is to work algorithms for deciding, given a conditional preference network (CP-net) and two outcomes, which outcome is more preferred. We will organize a competition, perhaps based on the ICAPS International Planning Competitions, for these algorithms. Secondly, we will explore two models of temporal preferences: Temporal Conditional Choice Networks (TCC-nets) and hidden Markov models (HMMs). We will develop an iPhone app, CommuteRoute, to collect individuals? choices over time of routes between home and work. This data, stored as feature vectors, will (in future work) allow us to test algorithms for learning and reasoning with TCC-nets, and comparing those algorithms to extant HMM algorithms.

偏好的计算模型对于推荐和决策支持系统非常重要，其应用包括长期规划者、智能家居和医疗监测器。 由于偏好表达的表达性和推理的容易性之间存在权衡，因此大多数个性化的基于偏好的系统依赖于相似个体的统计模型，或者一个个体的非常简单的模型。的偏好。 我们建议使用条件偏好模型，它可以表示更复杂的偏好。 我们的工作将是开发更好的算法来推理复杂结果或场景的偏好，并扩展模型，以便能够随着时间的推移对重复的选择进行推理。 此外，PI将使用偏好建模软件在她的推广到学校，大学和公众展示计算机可以建模和推理偏好的方式。 PI有支持不同群体的学生，包括来自LGBTQA社区的学生，有学习障碍的学生，妇女和有色人种的记录。因此，更广泛的影响包括推广和软件包，这将是公开的，用于推广人工智能和偏好处理，对偏好推理研究的基础设施的贡献，以及对计算机科学多样性的支持。 我们的建议的第一部分是工作的算法来决定，给定一个条件偏好网络（CP-网）和两个结果，哪一个结果是更喜欢。 我们将为这些算法组织一个竞赛，也许是基于ICAPS国际规划竞赛。 其次，我们将探讨两个模型的时间偏好：时间条件选择网络（TCC-网）和隐马尔可夫模型（HALTH）。 我们将开发一个iPhone应用程序，通勤路线，收集个人？家庭和工作之间的路线选择。 这些数据存储为特征向量，将（在未来的工作中）允许我们使用TCC网络测试学习和推理的算法，并将这些算法与现有的HMM算法进行比较。

项目成果

The Complexity of Campaigning

竞选活动的复杂性

• 发表时间: 2017
• 期刊: Algorithmic Decision Theory 2017
• 作者: Siler, Cory;Miles, Luke Harold;Goldsmith, Judy
• 通讯作者: Goldsmith, Judy

Uniform Random Generation and Dominance Testing for CP-Nets

• DOI: 10.1613/jair.5455
• 发表时间: 2017-08
• 期刊: J. Artif. Intell. Res.
• 作者: Thomas E. Allen;J. Goldsmith;Hayden Elizabeth Justice;Nicholas Mattei;Kayla Raines