Collaborative Research: CPS: Small: An Integrated Reactive and Proactive Adversarial Learning for Cyber-Physical-Human Systems

协作研究：CPS：小型：网络-物理-人类系统的集成反应式和主动式对抗学习

• 批准号: 2227185
• 负责人: Kyriakos G Vamvoudakis
• 金额: $ 25万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227185&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Small; Integrated; Collaborative; Research; CPS; Small; Integrated

The gradual deployment of self-driving cars will inevitably lead to the emergence of a new important class of cyber-physical-human systems where autonomous vehicles interact with human-driven vehicles via on-board sensors or vehicle-to-vehicle communications. Reinforcement learning along with control theory can help meet the safety requirements for real-time decision making and Level 5 autonomy in self-driving vehicles. However, it is widely known that conventional reinforcement learning policies are vulnerable to adversarial or non-adversarial perturbations to their observations, similar to adversarial examples for classifiers and/or reward (packet) drops of the learning. Such issues are exacerbated by concerns of addressing resiliency as the use of open communication and control platforms for autonomy becomes essential, and as the industry continues to invest in such systems. Decision making mechanisms, designed to incorporate agility with the help of reinforcement learning, allow self-adaptation, self-healing, and self-optimization. This research will contribute and unify the body of knowledge of several diverse fields including reinforcement learning, security, automatic control, and transportation for resilient autonomy with humans-in-the-loop.In this project, to counter action and observation manipulation as well as reward drops, the principal investigators will leverage proactive switching policies that aim (i) to provide robustness to adversarial inputs and reward drops in the closed-loop reinforcement learning mechanisms, (ii) to increase the cost of manipulation by deception, (iii) to limit the exposure of vulnerable actions and observations, and (iv) to provide stability, optimality, and robustness guarantees. Ultimately, the investigators will develop fundamental contributions to each of the above-mentioned fields and amalgamate these fields to provide a unique synthesis framework. The outcomes of this project will increase levels of confidence in autonomous technologies from ethical perspectives by providing an underpinning for curtailing accidents. The proposed framework can be extended to other key enablers of the global economy, including smart and connected cities, healthcare, and networked actions of smart systems while decreasing environmental pollution and minimizing the adverse environmental impacts on human health. The project will train the next generation of students from various levels, ages, and cultures through well-coordinated, level appropriate involvement in research and educational activities while providing a unique opportunity for the students to appreciate efficient, autonomous, and low-cost designs. This project will also contribute to future engineering curricula, pursue a substantial integration of research and education, and provide opportunities to engage students from the underrepresented group.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.

自动驾驶汽车的逐渐部署将不可避免地导致一类新的网络物理人类系统系统的出现，在该系统中，自动驾驶汽车通过车载传感器或车辆对车辆通信与人类驱动的车辆进行交互。强化学习与控制理论可以帮助满足实时决策的安全要求以及自动驾驶车辆中的5级自主权。但是，众所周知，常规的加强学习政策容易受到对抗性或非对抗性的观察，类似于分类器和/或奖励（小包）学习的对抗性示例。由于解决弹性和自治的控制平台的使用变得至关重要，并且随着行业继续投资于这样的系统，因此解决弹性的问题会加剧此类问题。决策机制，旨在借助加强学习，允许自我适应，自我修复和自我优化。这项研究将贡献和统一几个不同领域的知识体系，包括强化学习，安全性，自动控制和运输，以使其具有弹性的自主权，以应对该项目，以应对行动和观察操作以及奖励掉落，以及奖励掉落，首席研究人员将对固定的启动（i）的启用（i）奖励 - i）奖励 - i）奖励 - i）奖励 - i）奖励 - i）奖励 - i）奖励 - 奖励和奖励奖励，并奖励了奖励和奖励的奖励和奖励。机制，（ii）通过欺骗增加操纵成本，（iii）以限制脆弱的行动和观察的暴露，以及（iv）提供稳定性，最佳性和稳健性的保证。最终，研究人员将为上述每个领域做出基本贡献，并将这些领域合并以提供独特的综合框架。该项目的结果将通过提供限制事故的基础来从道德角度提高对自主技术的信心。提议的框架可以扩展到全球经济的其他关键推动因素，包括智能和连接的城市，医疗保健以及智能系统的网络行动，同时减少环境污染并最大程度地减少对人类健康的不利影响。该项目将通过协调良好的，适当的参与研究和教育活动来培训来自各个层次，年龄和文化的下一代学生，同时为学生提供了一个独特的机会，可以欣赏高效，自主和低成本的设计。该项目还将为未来的工程课程做出贡献，追求研究和教育的实质性融合，并为吸引人数不足小组的学生提供机会。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估的。