CAREER: Spatial-Temporal Imitation Learning

人类根据自己的“策略”做出日常决策（例如出租车司机的寻车过程和通勤者的交通方式选择）。理解和纳入人类决策策略将为不断增长的零工人口和运输市场带来重大利益。例如，从出租车司机、私人车辆司机和城市通勤者那里学习决策制定策略可以促进服务提供商（例如，出租车/叫车公司），以更好地为乘客服务，并使城市规划者能够设计更好的道路网络和交通路线，以满足城市旅客的需求。该项目的目标是开发，实施和评估一个统一的框架，从他们生成的移动数据中学习人类代理的决策策略，并应用程序来解释和激励他们的决策，以促进个人和社会福祉。此外，在这个项目中，研究人员将通过为本科生和研究生开发新课程，接触K-12学生，并吸引女性和代表性不足的少数民族，整合研究，教育和推广。从他们的流动数据中学习人类决策策略有几个技术挑战：人类决策策略可能随时间和空间而变化，即，时空动力学挑战所收集的移动性数据可以仅覆盖空间区域和时间段的一部分，即，时空稀疏性挑战大多数人类代理不是“专家”，因此生成的移动性数据是嘈杂和不确定的，即，非专家挑战 大量的人类代理在做出决策时相互作用，即，交互和可伸缩性挑战。人类的决策受到许多（有时是隐藏的）因素的支配，这使得很难从他们的决策策略中推断出可解释的信息，即，可解释性挑战人类代理对提供的激励有不同的反应，这使得很难设计有针对性的激励机制来考虑代理的固有决策策略和他们的在线反馈，即，激励设计挑战。该项目将解决这些研究挑战，包括开发一种新的时空模仿学习框架，用于从个人和交互式人类代理学习决策策略，以及一个交互式系统，为人类代理提供可解释的学习结果和在线激励，以促进他们的决策策略。时空模仿学习框架和相关算法通过从人类的移动数据中高效准确地发现人类的决策策略，有可能在数据和城市科学领域产生变革性的影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Humans make daily decisions based on their own "strategies" (such as taxi drivers' passenger-seeking processes and commuters' transit mode choices). Understanding and incorporating human decision-making strategies will bring significant benefits to the growing gig-worker population and transportation marketplace. For example, learning the decision-making strategies from taxi drivers, personal vehicle drivers, and urban commuters can facilitate the service providers (e.g., taxi/ride-hailing companies) to better serve the passengers, and enable the urban planners to design better road networks and transit routes to meet the needs of urban travelers. The goal of this project is to develop, implement, and evaluate a unified framework to learn the decision-making strategies of human agents from their generated mobility data, with applications to explain and incentivize their decisions to promote individual and societal well-being. Moreover, in this project, the investigator will integrate research, education, and outreach by developing new courses for both undergraduate and graduate students, reaching out to K-12 students, and engaging women and underrepresented minorities.There are several technical challenges to learn human decision-making strategies from their mobility data: Human decision-making strategies may vary over time and space, i.e., spatial-temporal dynamics challenge. The mobility data collected may cover only a part of the spatial regions and time periods, i.e., spatial-temporal sparsity challenge. Most human agents are not "experts", thus the generated mobility data are noisy and uncertain, i.e., non-expert challenge. A large number of human agents interact with each other when making decisions, i.e., interaction and scalability challenge. Human decisions are governed by many (sometimes hidden) factors, which make it hard to infer explainable information from their decision-making strategies, i.e., explainability challenge. Human agents have diverse reactions to offered incentives, making it hard to design targeted incentive mechanisms to consider both agents' inherent decision-making strategies and their online feedback, i.e., incentive design challenge. This project will address these research challenges, and include the development of a novel spatial-temporal imitation learning framework for learning decision-making strategies from individual and interactive human agents, and an interactive system that provides human agents with explainable learning results and online incentives to promote their decision-making strategies. The spatial-temporal imitation learning framework and associated algorithms have the potential to be transformative in both the data and urban sciences by enabling efficient and accurate discovery of human decision-making strategies from their mobility data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.