CRII: CPS: RUI: Cognizant Learning for Autonomous Cyber-Physical Systems

CRII：CPS：RUI：自主网络物理系统的认知学习

• 批准号: 2153136
• 负责人: Bhaskar Ramasubramanian
• 金额: $ 17.49万
• 依托单位: Western Washington University
• 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: 2022-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153136&HistoricalAwards=false
• 关键词: CRII; CPS; RUI; Cognizant; Learning

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The objective of this Computer and Information Science and Engineering (CISE) Research Initiation Initiative (CRII) proposal is to develop a cognizant learning framework for cyber-physical systems (CPS) that incorporates risk-sensitive and irrational decision making. The necessity for such a framework is exemplified by two observations. First, CPS such as self-driving cars will share an environment with other CPS and human users. Human drivers demonstrate a heightened sensitivity to changes in speed and can easily adapt to changes in the environment and road conditions, which makes it essential for a CPS to have an ability to recognize non-rational behaviors. Second, large amounts of data generated during their operation and limited access to models of their environments can make a CPS reliant on machine learning algorithms for decision making to meet performance requirements such as reachability and safety. Our research will be grounded on improving behaviors of autonomous vehicles in realistic traffic situations. Outcomes from this effort will contribute to the development of a research paradigm unifying control, learning, and behavioral economics. Students at a Primarily Undergraduate Institution will benefit by being directly involved in all aspects of the research process. Research tasks will involve a team of undergraduate students in a vertically integrated manner where more experienced students will mentor newer team members. The proposed effort comprises two thrusts. Thrust 1 will construct utilities to encode CPS performance objectives consistent with practical models of risk-sensitive and irrational decision making. Strategies will be learned by formulating and solving a reinforcement learning problem to maximize this utility. Methods to enable learned strategies to adequately consider delays between evaluation and execution of actions arising from the physical components of the CPS will be developed. Thrust 2 will design algorithms to learn decentralized cognizant strategies when multiple CPS operate in the same environment. To improve reliability in uncertain environments, or when feedback is sparse, techniques to identify contributions of each CPS to a shared utility will be identified. Solution methodologies will be evaluated empirically through extensive experiments and theoretically by determining probabilistic performance guarantees. The PI will develop a research agenda and new undergraduate curriculum in CPS and machine learning at Western Washington University (WWU). Research and educational goals of the project will be integrated through the CARLA simulator for autonomous vehicle research and the F1/10 Autonomous Vehicle platform. The multidisciplinary scope of the project will be emphasized in outreach efforts through Student Outreach Services and STEM Clubs at WWU to encourage and broaden participation from traditionally underrepresented student groups.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.

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。 这个计算机和信息科学与工程（CISE）研究启动计划（CRII）提案的目标是为网络物理系统（CPS）开发一个认知的学习框架，其中包括风险敏感和非理性的决策。有两点意见说明了这种框架的必要性。首先，自动驾驶汽车等CPS将与其他CPS和人类用户共享环境。人类驾驶员对速度的变化表现出高度的敏感性，并且可以很容易地适应环境和道路条件的变化，这使得CPS具有识别非理性行为的能力至关重要。其次，在其操作过程中生成的大量数据以及对其环境模型的有限访问可能使CPS依赖于机器学习算法进行决策，以满足可达性和安全性等性能要求。我们的研究将以改善自动驾驶汽车在现实交通情况下的行为为基础。这项工作的成果将有助于发展一个统一的控制，学习和行为经济学的研究范式。学生在一所私立本科院校将受益于直接参与研究过程的各个方面。研究任务将涉及一个垂直整合的本科生团队，更有经验的学生将指导新的团队成员。拟议的努力包括两个方面。推力1将构建公用事业编码CPS性能目标符合风险敏感和非理性决策的实际模型。策略将通过制定和解决强化学习问题来学习，以最大限度地提高这种效用。将制定方法，使学习战略充分考虑CPS物理组件所产生的行动的评估和执行之间的延迟。Thrust 2将设计算法来学习分散的认知策略，当多个CPS在同一环境中运行时。为了提高在不确定的环境中的可靠性，或当反馈是稀疏的，技术，以确定每个CPS的贡献，以共享的效用将被确定。解决方案的方法将通过大量的实验和理论上确定概率性能保证经验评估。PI将在西华盛顿大学（WWU）制定CPS和机器学习的研究议程和新的本科课程。该项目的研究和教育目标将通过用于自动驾驶汽车研究的CARLA模拟器和F1/10自动驾驶汽车平台进行整合。该项目的多学科范围将通过WWU的学生外展服务和STEM俱乐部在外展工作中得到强调，以鼓励和扩大传统上代表性不足的学生群体的参与。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。