BRITE Fellow: Autonomous Systems that Accommodate Human Perception and Reasoning about Uncertainty

这个促进工程变革和公平进步的研究思路（BRITE）研究员奖将资助研究，使自主系统和人类在不确定的环境中可预测的安全互动，应用于航空航天，制造业，运输和医疗保健系统，从而促进科学进步，促进国家繁荣，福利和国防。通过对环境的感知和反应，自主系统有可能帮助人类操作员更轻松、更安全地完成困难、危险或有风险的任务。然而，当自主系统看起来不可预测、不可靠或反应迟钝时，它们就会成为障碍，而不是帮助的来源。为了防止这种结果，该项目旨在为自主系统开发一种算法设计和控制框架，成功地适应人类行为不可避免的不可预测性，以及系统动态和行为的不确定性对人类决策的影响。这个框架的一个关键要素是它能够捕捉到人类的典型需求，从不确定的信息设计解决方案，并在潜在的冲突约束下，满足所需的目标，以最大可能的程度。本研究是整合课程的举措，旨在促进文化响应的教学和基于项目的学习，在培训来自不同背景的学生在以人为本的设计自主systems.This研究的目的是作出根本性的贡献，以随机为基础的方法论的方法整合知识的人类感知和推理的不确定性到自主动力系统的设计和控制。为此，将开发基于控制理论，机器学习和人为因素的新数学理论和计算算法。这种理论将解决任意的，非高斯形式的随机性和理性和非理性的决策模型的处理，重点是计算效率的控制器合成。还将探讨建设性地适应人类变化的方法的可行性和数学特性，并对上下文进行感知。所开发的算法和理论将在基于仿真的平台上进行实验验证。该项目使主要研究者能够利用在随机动力系统中确保概率安全的方法方面的专业知识，以及正在进行的将认知模型集成到自主系统中的工作，以追求以人为中心的自主性的高风险愿景，具有重大的变革潜力。该奖项反映了NSF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。

This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Fellow award will fund research that enables predictably safe interaction between autonomous systems and humans in uncertain environments, with applications in aerospace, manufacturing, transportation, and healthcare systems, thereby promoting the progress of science and advancing the national prosperity, welfare, and defense. By sensing and reacting to their environment, autonomous systems have the potential to help human operators accomplish difficult, dangerous, or risky tasks more easily and safely. However, when autonomous systems appear to be unpredictable, unreliable, or unresponsive, they become hindrances, rather than a source of help. To prevent this outcome, this project aims to develop an algorithmic design and control framework for autonomous systems that successfully accommodates the inevitable unpredictability of human actions, as well as the effects on human decision-making of uncertainty in the dynamics and action of the system. A key element of this framework is its ability to capture typical needs of humans to design solutions from uncertain information, and under potentially conflicting constraints, which meet desired objectives to the greatest possible degree. This research is integrated with curricular initiatives aiming to promote culturally responsive pedagogy and project-based learning in the training of students from diverse backgrounds in human-centric design of autonomous systems.This research aims to make fundamental contributions to a stochasticity-based methodological approach for integrating knowledge of human perception and reasoning about uncertainty into the design and control of autonomous dynamical systems. To this end, new mathematical theory and computational algorithms will be developed, based in control theory, machine learning, and human factors. Such theory will address the handling of arbitrary, non-Gaussian forms of stochasticity and rational and non-rational decision models, with emphasis on computationally efficient controller synthesis. The feasibility and mathematical properties of methods to constructively accommodate human variability, and to be context-aware, will also be explored. The developed algorithms and theories will be experimentally validated in simulation-based platforms. This project enables the principal investigator to leverage expertise in methods for assuring probabilistic safety in stochastic dynamical systems and ongoing work on integrating cognitive models in autonomous systems to pursue a high-risk vision for human-centric autonomy with significant potential for transformational impact. It lays a foundation for advancing culturally responsive teaching and research practices throughout the engineering community.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.