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: 2312659
• 负责人: Adam Eck
• 金额: $ 29.92万
• 依托单位: Oberlin College
• 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=2312659&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在现实世界中的决策变得复杂。除了不完善的感应外，由于他人的未知动作以及这些代理带来的动态，在共享上下文中还增加了不确定性。开放系统是那些实际情况，其组成由于内部或外部事件而随着时间的变化而变化。这项研究调查了决策者（即代理商）如何在开放系统中最好地在不确定性下作用。将探索三种形式的开放性。首先是代理人随着时间的推移进入或离开系统时。当代理必须完成的任务随时间变化时，第二次发生。当代理商的能力因学习新角色或技能而变化时，第三次发生。这三种形式的开放性虽然在现实世界中很普遍，并且在人类组织，灾难响应和智能运输等例子中发现，但以前尚未研究它们如何使决策变得复杂及其在实现人工智能应用中的重要作用。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该项目上合作。将发起一项新的评估计划，以创建涉及使用各种开放性的涉及使用启发的领域的竞争，以激发更广泛的兴趣。创新的课程模块基于创造性思维的原则，带来开放的挑战，以及我们如何向本科生和研究生致敬，将使该项目的成果能够整合到课堂上。该项目采取了调查框架的方法，以研究各种开放性的方法，以实现这些范围的研究，以实现这些范围的研究，以实现这些框架，以实现这些框架，以实现这些框架，以实现这些框架。涉及许多代理商的开放系统专注于理解任务和框架开放性的影响。最终目标是结合所有三种开放性的表示形式，并研究这是否使决策问题从根本上变得更加困难。将确定和利用每种开放性下的计划和学习技术之间的协同作用。当结合过去几十年在由于传感器噪声引起的不确定性下的决策中的进步结合在一起时，这些方法将代表将原则性的计划和学习转化为现实世界中环境的真实复杂性的变革步骤。这项奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和广泛的影响来评估NSF的法定任务，并被认为是珍贵的支持。