CPS: Medium: Collaborative Research: Data-Driven Modeling and Preview-Based Control for Cyber-Physical System Safety

CPS：中：协作研究：数据驱动的建模和基于预览的网络物理系统安全控制

• 批准号: 1932254
• 负责人: Samet Oymak
• 金额: $ 29万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932254&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Data

This project will develop the theory and algorithmic tools for the design of provably-safe controllers that can leverage preview information from different sources. Many autonomous or semi-autonomous cyber-physical systems (CPS) are equipped with mechanisms that provide a window of projecting into the future. These mechanisms can be forward looking sensors like cameras (and corresponding perception algorithms), map information, forecast information, or more complicated predictive models of external agents learned from data. Through these mechanisms, at run-time, the systems have a preview of what lies ahead. Leveraging this information to improve performance of CPS while keeping strong guarantees on their safety, therefore, holds great promise for multiple technologies of national interest. We will use driver-assist systems in connected vehicles as the main application. Education and outreach activities will involve undergraduate and graduate students along with stakeholders from local automotive companies.To develop the theory for learning- and prediction-enabled safety for CPS we will: (i) develop a modeling formalism, namely preview automata, for systems with preview information and correct-by-construction control algorithms that consider structured inaccuracies in the predictions for resilience; (ii) investigate how cooperation can assist in enriching the preview information; (iii) learn, via finite-sample data analysis, trustworthy dynamical models of the behaviors of non-cooperative agents with provable uncertainty bounds; and (iv) design methods for selecting compatible models from the learned dynamical models and for deriving safe controllers in the presence of cooperative and non-cooperative agents. Our innovations will enable safety-critical CPS to take full advantage of emerging technologies on sensing, perception, communication, and learning.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.

该项目将开发用于设计可证明安全的控制器的理论和算法工具，这些控制器可以利用来自不同来源的预览信息。许多自主或半自主网络物理系统（CPS）都配备了提供预测未来窗口的机制。这些机制可以是前瞻性传感器，如摄像机（和相应的感知算法）、地图信息、预测信息，或更复杂的从数据中学习的外部代理的预测模型。通过这些机制，系统在运行时可以预览未来的情况。因此，利用这些信息来提高 CPS 的性能，同时强有力地保证其安全性，为多种符合国家利益的技术带来了巨大的希望。我们将在联网车辆中使用驾驶辅助系统作为主要应用。教育和推广活动将涉及本科生和研究生以及当地汽车公司的利益相关者。为了开发 CPS 的学习和预测安全理论，我们将：(i) 开发一种建模形式，即预览自动机，适用于具有预览信息和构建修正控制算法，考虑弹性预测中的结构性错误； (ii) 研究合作如何有助于丰富预览信息； (iii) 通过有限样本数据分析，学习具有可证明的不确定性界限的非合作主体行为的值得信赖的动态模型； （iv）设计方法，用于从学习的动态模型中选择兼容模型，并在存在合作和非合作代理的情况下导出安全控制器。我们的创新将使安全关键的 CPS 能够充分利用传感、感知、通信和学习方面的新兴技术。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。

项目成果

Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks

• DOI: 10.1609/aaai.v35i8.16859
• 发表时间: 2020-12
• 作者: Xiangyu Chang;Yingcong Li;Samet Oymak;Christos Thrampoulidis

Label-Imbalanced and Group-Sensitive Classification under Overparameterization

• 发表时间: 2021-03
• 作者: Ganesh Ramachandra Kini;Orestis Paraskevas;Samet Oymak;Christos Thrampoulidis

Stochastic Contextual Bandits with Long Horizon Rewards

具有长期奖励的随机上下文强盗

• 发表时间: 2023
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Qin, Yuzhen;Li, Yingcong;Pasqualetti, Fabio;Fazel, Maryam;Oymak, Samet
• 通讯作者: Oymak, Samet

Learning a deep convolutional neural network via tensor decomposition

• DOI: 10.1093/imaiai/iaaa042
• 发表时间: 2021-02
• 期刊: Information and Inference: A Journal of the IMA
• 作者: Samet Oymak;M. Soltanolkotabi

Unsupervised Paraphrasing via Deep Reinforcement Learning

• DOI: 10.1145/3394486.3403231
• 发表时间: 2020-07
• 期刊: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• 作者: A.B. Siddique;Samet Oymak;Vagelis Hristidis