CAREER: Decision Procedures for High-Assurance, AI-Controlled, Cyber-Physical Systems

本项目探索新的数学技术，为理解人工智能（AI）控制的信息物理系统（CPS）的基本特性并指导其设计提供科学基础。从简单的逻辑结构到复杂的深度神经网络模型，人工智能代理越来越多地控制物理/机械系统。自动驾驶汽车、无人机和智能城市都是人工智能控制的CPS的例子。然而，尽管人工智能在众多CPS领域的使用呈爆炸式增长，但这些人工智能控制的CPS的安全性和可靠性仍然是一个有待研究的问题。该项目包括与教育相结合的活动，以探索通过反例学习如何适用于人工智能，并帮助年轻学生培养批判性思维技能。该项目研究了能够同时分析CPS的网络组件（包括ai代理）和物理组件的新一代形式化方法工具。这种新一代的形式化方法将用于分析人工智能控制的CPS的安全性和可靠性，表征系统保证正确运行的环境，并实时预测其故障。此外，本项目将解决人工智能控制的CPS系统故障的责任分配问题。提议的方法将在两个旗舰测试平台(i)自动驾驶无人机和（ii）自动驾驶汽车上进行评估。该项目涉及研究生、本科生和高中生，并通过提供算法的开源实现与科学界接触。该奖项反映了美国国家科学基金会的法定使命，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 project explores new mathematical techniques that provide a scientific basis to understand the fundamental properties of Cyber-Physical Systems (CPS) controlled by Artificial Intelligence (AI) and guide their design. From simple logical constructs to complex deep neural network models, AI agents are increasingly controlling physical/mechanical systems. Self-driving cars, drones, and smart cities are just examples of AI-controlled CPS. However, regardless of the explosion in the use of AI within a multitude of CPS domains, the safety and reliability of these AI-controlled CPS is still an under-studied problem. This project includes activities integrated with education, so as to explore how learning through counterexamples works for AI, and to help with critical thinking skills for young students.This project investigates a new generation of formal method tools that are capable of simultaneously analyzing the cyber components (including AI-agents) and the physical components of CPS. This new generation of formal methods will be used to analyze the safety and reliability of AI-controlled CPS, characterize the environments for which the system is guaranteed to operate correctly and predict their failure at real-time. In addition, this project will address the problem of how to assign the blame of system failures in AI-controlled CPS. The proposed methods will be evaluated over two flagship testbeds (i) autonomous drones and (ii) self-driving cars. The project engages graduate, undergraduate, and high-school students, and reaches out to the scientific community by providing open source implementations of algorithms.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.