HCC: Small: Leadership Emergence for Synchronous Human-Autonomy Teaming

机器的自主性越来越强，能够在很少或没有监督的情况下执行许多复杂的任务。然而，在许多真实世界的应用程序中，他们仍然经常需要与人类一起工作，在这些应用程序中，人类提供多样化、冗余和互补的技能，使系统更具弹性。因此，在无人驾驶汽车和医疗诊断等新兴应用中，能够建造能够与人类很好地合作的自动机器将变得越来越重要。该项目的研究方向是允许机器像人类在大型组织中所做的那样进行协作，特别是在领导、跟踪和与他人同步方面，以在日常和紧急情况下完成关键任务。最终，该项目将增强智能机器与人类合作的能力，以改进业务运营并以稳健可靠的方式服务公众。该项目旨在定义领导力的紧急性质和认知同步，以将自治整合到多智能体组织中。项目团队将以隐性领导理论为基础开展工作，该理论预测，无论预定义的权力结构如何，人类团队中的领导和遵循动态往往会导致将领导权授予最有能力和最值得信赖的成员。为了利用这一理论，研究人员将开发方法，根据基于技能、规则和知识的行为来支持不同任务的认知同步，这些行为对人类认知进行分类，并作为信任评估的基础。将开发强化学习方法，用于选择交互方法和改进交互选择，以促进长期、富有成效的合作。这些方法将在计算机模拟和现场实验中进行评估，无人驾驶车辆和人类队友将在搜索和救援任务中进行评估。设计原则和算法将适用于许多涉及指挥和控制的领域，如灾难响应、空中交通管制和病人流量管理。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。

Machines are increasingly autonomous, capable of performing many complex tasks with little or no supervision. However, they still often need to work on teams with humans in many real-world applications, where humans provide diverse, redundant, and complementary skills that make the systems more resilient. Thus, being able to build autonomous machines that can team well with humans will become increasingly important in emerging applications such as driverless cars and medical diagnosis. The research in this project moves toward allowing machines to collaborate as humans do in large organizations, especially in terms of leading, following, and synchronizing with others to complete critical tasks in both routine and emergency situations. Ultimately, the project will enhance the abilities of intelligent machines to work with humans to improve business operations and serve the public in a robust and reliable manner.The project seeks to define the emergent nature of leadership and cognitive synchronization for integrating autonomy into multi-agent organizations. The project team will ground the work in implicit leadership theory, which predicts that leading and following dynamics in human teams tend to result in granting leadership to the most competent and trustworthy member regardless of a pre-defined authority structure. To leverage this theory, the researchers will develop methods to support cognitive synchronization across diverse tasks according to skill-, rule-, and knowledge-based behaviors that classify human cognition and underlie assessments of trust. Reinforcement learning approaches will be developed for selecting interaction methods and improving interaction choices to promote long-term, productive collaboration. These methods will be evaluated in computer simulations and field experiments with unmanned vehicles and human teammates in the context of search and rescue missions. The design principles and algorithms will be applicable to many domains involving command and control with humans in the loop, such as disaster response, air traffic control, and patient flow management.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.