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

各种类型的不确定性使现实环境中的决策复杂化。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性，在共享的上下文中增加了不确定性。开放系统是那些真实世界的环境，其组成会随着时间的推移而变化，无论是内部还是外部事件。这项研究调查了决策者（即，代理人）在开放系统中可能最好在不确定性下行动。将探索三种开放形式。第一种是代理随着时间的推移进入或离开系统。第二种情况发生在必须由代理完成的任务随时间变化时。 第三种情况发生在智能体的能力因学习新角色或技能而发生变化时。 尽管这三种开放形式在真实的世界中普遍存在，并在人类组织、灾难响应和智能交通等例子中发现，但以前没有研究过它们如何使决策复杂化，以及它们在人工智能应用中的重要作用。来自格鲁吉亚大学和内布拉斯加-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，以激发更广泛的兴趣，从而创建一个涉及展示各种类型开放性的使用启发域的竞赛。基于创造性思维原则的创新课程模块，为本科生和研究生带来开放性的挑战以及如何应对这些挑战，将使本项目的成果融入课堂。本项目采用调查框架的方法，为各种类型的开放性建模，并在这些框架的背景下实现最佳行为的方法。具体来说，研究人员将继续研究如何将自动规划和强化学习扩展到涉及许多代理的开放系统，并重点关注任务和框架开放性的影响。最终的目标是将所有三种形式的开放性的联合收割机表现结合起来，并研究这是否会从根本上使决策问题变得更加困难。将确定和利用每种开放方式下的规划和学习技术之间的协同作用。结合过去几十年在传感器噪声不确定性下的决策方面取得的进步，这些方法将代表着将原则性规划和学习转化为现实世界环境真正复杂性的变革性一步。该奖项反映了NSF的法定使命，并被认为值得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来支持。

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.