EAGER: CNS: Misdirection in Robot Teams: Exploiting Organizational Principles for Operational Advantage

信任、可靠性、凝聚力和能力是一个有效团队的组成部分。这些属性对于协作的机器人和人类团队以及全机器人团队都是相同的。对于一个团队来说，要想在存在错误、失败和错误的情况下保持有效，团队成员必须对问题和可能的解决方案有足够的认识和理解，以便在他们的操作中做出适当的调整。当多个具有相互竞争动机的团队被分配任务时，一个可行的策略可能是削弱、影响或动摇其他团队的属性，并限制他们对自己全部选择的理解。这种策略广泛存在，例如在自然界、体育竞赛和军事行动中，经常出现佯攻和误导等策略。本项目关注多机器人的一类高级策略：即误导和在可行的情况下对抗误导。随着多机器人系统变得更加自治、分布式、网络化、数量众多，并具有更大的决策能力，必须预测有意和无意误导的前景。该项目将在安全和军事行动等重要战略领域支持强大的多机器人作战。该项目研究使协作机器人、多机器人和多机器人团队能够在各种情况下建模、生成和处理误导的策略。机器人控制的这个研究方向为这些团队内部和团队之间应对这些可能的破坏形式提供了一种新的方法。计算模型，特别是从人类努力和群体行为的研究中得出的，为理解、产生和对抗机器人系统中的误导提供了一个总体框架。将使用个人和团队层面的递归模式理论行为模型设计计算模型框架，以分散的控制和通信方法为基础，在嘈杂和混乱的环境中提供鲁棒性。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。

Trust, dependability, cohesion, and capability are integral to an effective team. These attributes are the same for teams of collaborating robots and humans as well as all-robot teams. For a team to remain effective in the presence of faults, failures, and errors from nature or intent, team members must have sufficient perception and understanding of the problem and possible solutions to make appropriate adjustments in their operations. When multiple teams with competing incentives are tasked, a strategy, if available, may be to weaken, influence or sway the attributes of other teams and limit their understanding of their full range of options. Such strategies are widely found, for example, in nature, sporting contests, and military operations, where strategies such as feints and misdirection often appear. This project focuses on one class of higher-level strategies for multi-robots: namely, to misdirect and where feasible to counter misdirection. As multi-robot systems become more autonomous, distributed, networked, numerous, and with more capability to make critical decisions, the prospect for intentional and unintentional misdirection must be anticipated. This project will support robust multi-robotic operations in important strategic domains such as security and military operations. The project studies strategies to enable co-robots, multi-robots and teams of multi-robots to model, generate, and cope with misdirection in various situations. This research direction in robotic control offers a novel approach to resilience in and among these teams to these forms of possible disruption. Computational models, drawn particularly from studies of human endeavors and group behaviors, provide a general framework for understanding, producing, and countering misdirection in robotic systems. A framework of computational models will be designed using recursive schema-theoretic models of behaviors at the individual and team levels, building on decentralized methods of control and communication, to provide robustness in the presence of noisy and chaotic environments.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.