RI: Small: Foundations and Applications of Observer-Aware Planning

人工智能(AI)在我们的日常生活中变得无处不在。人类和人工智能之间的这种频繁交互，要求系统更好地认识环境中的人类。该项目开发了一种在人类观察员在场的情况下进行人工智能决策的综合方法。如果不考虑观察者，人工智能系统的行为可能会让其他人感到困惑、震惊，甚至威胁到其他人。例如，人类经常在观察者面前故意改变他们的行为，以使他们的意图变得透明，并让观察者放心。观察者感知行为可以包括例如使用手势或光信号来传达意图的显性交流，以及通过行为的隐含交流。该项目统一了广泛的观察者感知行为，旨在实现不同的目标，包括通过选择动作隐含传达意图的清晰行为，符合观察者预期的可解释行为，使观察者能够预测未来行动的可预测行为，以及旨在揭示代理能力的行为。这些形式的观察者感知行为推动了当代人工智能的一个关键研究重点，即产生更容易理解、预测和协作的以人为中心的系统。本项目引入了一个新的模型来研究观察者感知规划，称为观察者感知马尔可夫决策过程(OAMDP)。该模型开发了新的自动化规划技术，可以优化现有规划技术范围之外的观察者感知目标。具体贡献包括：(1)分析不同假设下观察者感知规划的计算复杂性以及OAMDP与现有模型之间的理论和实践差异；(2)为有效的观察者感知规划开发精确和近似算法；(3)实现和评估与人类感知人工智能系统兼容的信念更新方法；以及(4)扩展该方法，以管理与观察者相关的目标(例如，提高意图的可预测性)与与领域相关的目标(例如，减少所分配任务的完成时间)之间的权衡，并纳入智能体和观察者之间的显式交流。该项目的总体目标是开发一种通用的自动化规划方法，可以实现一系列与观察者相关的战略目标，分析这些问题的理论复杂性，开发高效的观察者感知规划算法，并在现实环境中通过人类受试者的实验来验证它们。该团队对新的规划范式进行了全面评估，并在涉及观察员与移动机器人和自动驾驶车辆互动的现实环境中实验演示了观察者感知规划的价值，包括与行业合作伙伴合作开发的用例。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Artificial Intelligence (AI) is becoming ubiquitous in our daily lives. This frequent interaction between humans and AI, requires systems to be more cognizant of humans in the environment. This project develops a comprehensive approach for AI decision making in the presence of human observers. Without considering observers, AI systems could behave in a way that confuses, startles, or even threatens others. Humans often change their behavior in the presence of observers in a deliberate way, for example, to make their intention transparent and reassure an observer. Observer-aware behaviors may include explicit communication to convey intentions, for example, using hand gestures or light signals, as well as implicit communication through behavior. The project unifies a wide range of observer-aware behaviors designed to accomplish different goals, including legible behavior that implicitly conveys intentions via the choice of actions, explicable behavior that conforms to observers’ expectations, predictable behaviors that enable observers to predict future actions, as well as behaviors designed reveal the capabilities of the acting agent. These forms of observer-aware behaviors advance a key research priority in contemporary AI; that is, to produce human-centered systems that are easier to understand, predict, and collaborate.This project introduces a new model to study observer-aware planning called Observer-Aware Markov Decision Process (OAMDP). This model develops novel automated planning techniques that can optimize observer-aware objectives beyond the scope of existing planning techniques. Specific contributions include: (1) analyses of the computational complexity of observer-aware planning under different assumptions and the theoretical and practical differences between OAMDP and existing models; (2) development of both exact and approximate algorithms for efficient observer-aware planning; (3) implementation and evaluation of belief-update methods compatible with how humans perceive AI systems; and (4) extensions of the approach to manage the tradeoffs between observer-related objectives (e.g., improving predictability of intentions) and domain-related objectives (e.g., reducing the completion time of the assigned task) and incorporate explicit communication between the agent and the observer. The overarching goals of this project are to develop a general automated planning approach that can achieve a range of observer-related strategic objectives, analyze the theoretical complexity of these problems, develop efficient algorithms for observer-aware planning, and validate them via experiments with human subjects in realistic settings. The team conducts a comprehensive evaluation of the new planning paradigm and demonstrates experimentally the value of observer-aware planning in realistic settings involving observers interacting with mobile robots and autonomous vehicles, including use cases developed in collaborations with industry partners.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.