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: 2312657
• 负责人: Prashant Doshi
• 金额: $ 46.71万
• 依托单位: University of Georgia Research Foundation Inc
• 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=2312657&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，除了不完美的感知之外，由于其他人的未知行为以及这些代理带来的活力，共享环境中还存在额外的不确定性。本研究探讨了开放系统中决策者（即主体）如何在不确定性下采取最佳行动。第一个是主体何时进入或开放。离开系统第二种是当代理必须完成的任务随着时间的推移而发生变化时，第三种是当代理的能力因学习新的角色或技能而发生变化时，这三种形式的开放性在现实世界中都很普遍。佐治亚大学、内布拉斯加大学林肯分校和来自乔治亚大学的研究人员之前从未研究过人类组织、灾难响应和智能交通等例子如何使决策变得复杂以及它们在实现人工智能应用中的重要作用。欧柏林学院将就此进行合作将推出一项新的评估举措，以创建展示各种类型开放性的竞赛使用启发领域，以激发更广泛的兴趣，该模块基于创造性思维原则，带来开放性的挑战以及我们如何应对。向本科生和研究生提出这些问题将使该项目的成果融入课堂。该项目采用研究框架的方法来建模各种类型的开放性，并实现在这些框架的背景下采取最佳行动的方法。研究人员将继续研究扩展自动化开放系统的规划和强化学习涉及许多智能体，重点关注理解任务和框架开放性的影响，最终目标是结合所有三种形式的开放性的表示，并研究这是否从根本上使决策问题变得更加困难。每种开放性下的规划和学习技术之间的差异将被识别和利用，当与过去几十年在传感器噪声导致的不确定性下的决策方面取得的进展相结合时，这些方法将代表着将原则性规划和学习技术转化为变革性的一步。学习真正的复杂性该奖项反映了 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。