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

RI：小型：观察者感知规划的基础和应用

• 批准号: 2205153
• 负责人: Shlomo Zilberstein
• 金额: $ 60万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2205153&HistoricalAwards=false
• 关键词: RI; Small; Foundations; Applications; Observer

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.

人工智能（AI）在我们的日常生活中变得无处不在。人类与人工智能之间的这种频繁互动，要求系统更加了解环境中的人类。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。在不考虑观察者的情况下，人工智能系统可能会以一种令人困惑、震惊甚至威胁他人的方式行事。人类经常会在观察者面前故意改变自己的行为，例如，让他们的意图透明化，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式通信，以及通过行为的隐式通信。该项目统一了各种各样的感知行为，旨在实现不同的目标，包括清晰的行为，隐含地传达意图通过选择的行动，可解释的行为，符合观察者的期望，可预测的行为，使观察者能够预测未来的行动，以及行为设计揭示代理的能力。这些形式的感知行为推进了当代人工智能的一个关键研究优先事项;即，产生更容易理解，预测和协作的以人为中心的系统。该项目引入了一个新的模型来研究感知规划，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感知目标。具体贡献包括：（1）分析不同假设下的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）开发有效感知规划的精确和近似算法;（3）实现和评估与人类如何感知AI系统兼容的信念更新方法;以及（4）扩展该方法以管理与交易相关的目标之间的权衡（例如，提高意图的可预测性）和域相关目标（例如，减少所分配任务的完成时间），并在代理和观察者之间引入显式通信。该项目的总体目标是开发一种通用的自动化规划方法，可以实现一系列与自动化相关的战略目标，分析这些问题的理论复杂性，开发用于自动化感知规划的有效算法，并通过在现实环境中与人类受试者进行实验来验证它们。该团队对新的规划范式进行了全面评估，并通过实验证明了观察者与移动的机器人和自动驾驶汽车交互的现实环境中的感知规划的价值，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.