Collaborative Research: RI: Medium: RUI: Automated Decision Making for Open Multiagent Systems

协作研究：RI：中：RUI：开放多智能体系统的自动决策

• 批准号: 2312658
• 负责人: Leen-Kiat Soh
• 金额: $ 42.49万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312658&HistoricalAwards=false
• 关键词: Collaborative; Research; RI; Medium; RUI

Various types of uncertainties complicate decision making in real-world contexts. In addition to imperfect sensing, there is added uncertainty in shared contexts due to the unknown actions of others and the dynamism brought about by these agents. Open systems are those real-world contexts whose composition changes over time due to either internal or external events. This research investigates how decision-makers (i.e., agents) may best act under uncertainty in open systems. Three forms of openness will be explored. The first is when the agents enter or leave the system over time. The second occurs when the tasks that must be completed by agents change over time. The third occurs when the agents’ capabilities change from learning new roles or skills. All three forms of openness, though prevalent in the real world and found in examples such as human organizations, disaster response, and smart transportation, have not been studied previously with respect to how they complicate decision making and their important role in enabling applications of artificial intelligence. Researchers from the Universities of Georgia and Nebraska-Lincoln, and from Oberlin College, will collaborate on this project. A new evaluation initiative leading into the creation of a competition involving use-inspired domains exhibiting various types of openness will be launched to spur broader interest. An innovative lesson module based on principles of creative thinking that brings the challenges of openness and how we may address them to undergraduate and graduate students will allow this project’s outcomes to be integrated into the classroom.The project takes the approach of investigating frameworks for modeling the various types of openness and realizing methods for acting optimally in the context of these frameworks. Specifically, the researchers will continue their investigations into scaling automated planning and reinforcement learning to open systems involving many agents with a novel focus on understanding the impact of task and frame openness. The ultimate goal is to combine representations of all three forms of openness and study whether this makes the decision-making problem fundamentally harder. Synergies between the planning and learning techniques under each type of openness will be identified and exploited. When combined with the advances of the past couple of decades in decision making under uncertainty due to sensor noise, these methods will represent a transformative step in translating principled planning and learning to the true complexities of real-world contexts.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.

各种类型的不确定性使现实世界中的决策变得复杂。除了不完善的感知之外，由于其他人的未知行为以及这些代理带来的动态性，共享上下文中还存在着额外的不确定性。开放系统是指其组成因内部或外部事件而随时间变化的真实世界环境。这项研究调查了在开放系统中，决策者(即代理人)如何在不确定的情况下采取最好的行动。将探索三种形式的开放。第一个是代理随时间进入或离开系统的时间。第二种情况发生在必须由代理完成的任务随时间发生变化时。第三种情况发生在代理的能力因学习新角色或技能而发生变化时。尽管这三种形式的开放在现实世界中很普遍，并在人类组织、灾难应对和智能交通等例子中找到，但关于它们如何使决策复杂化以及它们在支持人工智能应用方面的重要作用，以前还没有人研究过。来自佐治亚大学和内布拉斯加州-林肯大学以及奥伯林学院的研究人员将在这个项目上进行合作。将发起一项新的评估倡议，以引起更广泛的兴趣，从而发起一场竞赛，涉及展示各种类型的开放性的受使用启发的领域。一个基于创造性思维原则的创新课程模块将给本科生和研究生带来开放的挑战，以及我们如何应对这些挑战，从而使本项目的结果能够整合到课堂上。该项目采取的方法是调查框架，以模拟各种类型的开放，并实现在这些框架的背景下采取最佳行动的方法。具体地说，研究人员将继续他们的研究，将自动规划和强化学习扩展到涉及许多代理的开放系统，并将新的重点放在理解任务和框架开放性的影响上。最终目标是将所有三种开放形式的表现形式结合起来，并研究这是否会使决策问题从根本上变得更加困难。将确定和利用每种类型的开放下的规划和学习技术之间的协同作用。与过去几十年在传感器噪声引起的不确定性下的决策制定方面的进步相结合，这些方法将代表着将原则性规划和学习转化为真实世界环境的真正复杂性的变革性步骤。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。