EAGER: Preferences in Repeated Choices

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、智能家居和医疗保健监视器。因为偏好表示的表达性和推理的便利性之间存在权衡，大多数个性化的基于偏好的系统要么依赖于相似个体的统计模型，要么依赖于一个个体的非常简单的模型。年代的偏好。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，在给定这样的模型的情况下，对复杂结果或场景的偏好进行推理，并扩展模型，使其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向学校、大学和普通公众展示计算机可以建模和推理偏好的方法。PI有支持不同学生群体的记录，包括LGBTQA社区的学生、有学习障碍的学生、女性和有色人种。因此，更广泛的影响包括外联和软件包，这将是公开的，用于人工智能和偏好处理的外联，对偏好推理研究基础设施的贡献，以及对计算机科学多样性的支持。我们建议的第一部分是工作算法来决定，给定一个条件偏好网络（CP-net）和两个结果，哪个结果更受欢迎。我们将为这些算法组织一场竞赛，也许是基于ICAPS国际规划竞赛。其次，我们将探讨两种时间偏好模型：时间条件选择网络（TCC-nets）和隐马尔可夫模型（hmm）。我们将开发一个名为“通勤路线”的iPhone应用程序来收集个人信息。随着时间的推移，家庭和工作之间的路线选择。这些数据以特征向量的形式存储，将（在未来的工作中）允许我们测试使用TCC-nets进行学习和推理的算法，并将这些算法与现有的HMM算法进行比较。

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.