CHS: Small: Optimizing Human-Machine Performance via Neurofeedback and Adaptive Autonomy

人类和自主人工智能(AI)系统之间的互动增加，正在从根本上改变我们的社会。然而，除非我们理解智能机器和人类应该如何最好地互动和沟通，否则在我们的生活中增加自主性是不会成功的。今天的人机交流几乎完全是语言的，在Siri或Alexa等系统上使用口语，或者在聊天机器人上使用打字文本。然而，人类之间的交流非常高效，你唱的不仅仅是文字；例如，通过对面部表情、手势、步态和语调的敏感。事实上，伟大的团队，无论是运动队还是军事格斗队，都擅长预测队友的行为和心态。在这个项目中，研究人员考虑了关于如何与自主人工智能合作的人类的认知和生理状态进行交流的基本科学和技术问题。该项目具有非常广泛的意义，因为它解决了与人和智能机器之间的交互相关的基本问题。该项目调查了这样一个假设，即自适应自主和协调的神经反馈可以在同一系统中使用，以优化人机性能。研究人员将开发一个框架，并在边界回避任务(BAT)的背景下调查假设，BAT是一类任务，其中任务关键边界包围着控制系统的最佳操作点。当考虑到人类控制时，这些任务特别有趣，因为它们通常会导致一个正反馈循环，系统地增加人类主体的唤醒状态，导致越来越差的任务表现和最终的任务失败，这符合耶克斯-道德森定律。我们的框架使用脑机接口(BCI)，既可以发挥自主性，又可以作为神经反馈的来源，将人类受试者转移到他们的表现“甜蜜点”。该项目将推动人机系统如何优化集成的科学和技术发展，特别是当1)机器可以在任务执行期间获得人类认知和生理状态的持续变化，以及2)通过适当的神经反馈使人类知道自己的状态时。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。

Our society is being fundamentally transformed by increased interaction between humans and autonomous artificial intelligence (AI) systems. However, the addition of autonomy to our lives will not be successful unless we understand how smart machines and humans should best interact and communicate. Human-machine communication today is almost entirely linguistic, using spoken language for systems such as Siri or Alexa, or typed text for chatbots. However, humans communicate extremely efficiently with each other byu sing much more than just words; for example by being sensitive to facial expression, gestures, gait, and intonation. In fact, great teams, whether sports teams or military combat teams, are excellent at predicting teammates behavior and state of mind. In this project, the investigators consider both basic science and technology questions with respect to how to communicate that cognitive and physiological state of a human that is co-operating with an autonomous AI. The project has very broad implications since it addresses fundamental questions related to the interactions between humans and smart machines.The project investigates the hypothesis that adaptive autonomy together with coordinated neurofeedback can be employed in the same system to optimize human-machine performance. Investigators will develop a framework and investigate the hypothesis within the context of boundary avoidance tasks, or BAT, which is a class of tasks in which task critical boundaries surround the optimal operating point of the control system. These tasks are particularly interesting when considering human control because they typically result in a positive feedback loop that systematically increases the arousal state of the human subject, resulting in increasingly poor task performance and ultimate task failure, consistent with the Yerkes-Dodson law. Our framework uses a brain-computer interface (BCI) to both engage autonomy as well as being a source for neurofeedback that can shift human subjects to their performance 'sweet-spot'. This project will advance the science and technology development of how human-machine systems can be optimally integrated, specifically when both 1) the machine has access to ongoing changes in human cognitive and physiological state during performance of the task and 2) the human is made aware of their own state via appropriate neurofeedback.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.