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

在现实环境中，各种类型的不确定性使决策复杂化。除了不完美的感知之外，由于其他人的未知行为和这些代理带来的动态，在共享环境中还存在额外的不确定性。开放系统是那些真实世界的上下文，它们的组成由于内部或外部事件而随时间变化。本研究探讨在开放系统的不确定性下，决策者（即代理人）如何采取最佳行动。探索三种开放形式。首先是代理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变化时。第三个阶段发生在代理因学习新角色或技能而能力发生变化的时候。这三种形式的开放，虽然在现实世界中普遍存在，并在人类组织、灾难响应和智能交通等例子中发现，但在它们如何使决策复杂化以及它们在实现人工智能应用中的重要作用方面，以前没有研究过。来自乔治亚大学、内布拉斯加州林肯大学和奥伯林学院的研究人员将在这个项目上合作。为了激发更广泛的兴趣，将启动一项新的评估计划，以创建一个涉及展示各种开放类型的使用启发式域名的竞赛。一个基于创造性思维原则的创新课程模块带来了开放性的挑战，以及我们如何向本科生和研究生解决这些挑战，将使这个项目的成果融入课堂。该项目采用的方法是研究各种类型的开放建模框架，并实现在这些框架的背景下采取最佳行动的方法。具体来说，研究人员将继续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，重点是理解任务和框架开放性的影响。最终目标是将所有三种形式的开放结合起来，并研究这是否会使决策问题从根本上变得更加困难。将确定和利用每种开放类型下的规划和学习技术之间的协同作用。当与过去几十年在传感器噪声不确定性下的决策制定方面的进展相结合时，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革命性步骤。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.