CAREER: Establishing correctness of learning-enabled autonomous systems with conflicting requirements

职业：建立具有冲突需求的学习型自治系统的正确性

• 批准号: 2141153
• 负责人: Tichakorn Wongpiromsarn
• 金额: $ 50.24万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2141153&HistoricalAwards=false
• 关键词: CAREER; Establishing; correctness; learning; enabled

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Autonomous systems are subject to multiple regulatory requirements due to their safety-critical nature. In general, it is infeasible to guarantee the satisfaction of all requirements under all conditions. In such situations, the system needs to decide how to prioritize among them. Two main factors complicate this decision. First, the priorities among the conflicting requirements may not be fully established. Second, the decision needs to be made under uncertainties arising from both the learning-based components within the system and the unstructured, unpredictable, and non-cooperating nature of the environments. Therefore, establishing the correctness of autonomous systems requires specification languages that capture the unequal importance of the requirements, quantify the violation of each requirement, and incorporate uncertainties faced by the systems.The proposed effort targets a major gap in theoretical foundations and computational tools to enable practical applications of formal methods throughout the development process of autonomous systems that include learning-based components, operate in uncertain environments, and are subject to conflicting requirements with partially established priorities. Its key novelty lies in the development of (1) probabilistic rulebooks, a new specification formalism that captures the tradeoffs between the uncertainty and the degree of violation of the requirements and utilizes such violation risk together with partially established priorities among the requirements to establish a consistent order among the trajectories of the system, (2) minimum-violation control synthesis algorithms that minimize the total violation risk based on the probabilistic rulebooks and allow learning-based functionality to be incorporated in a provably correct manner, and (3) quantitative verification frameworks that utilize statistical analysis of the learning-based components and the environment as well as the probabilistic rulebooks to provide quantitative analysis of the system. Such development will serve as a critical step towards establishing assurance of autonomous systems. This project will validate the theoretical results and algorithms developed under the proposed effort on a small autonomy platform that will also be utilized for educational purposes.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。 由于其安全关键的性质，自治系统受到多种监管要求的约束。一般来说，不可能保证在所有条件下满足所有要求。在这种情况下，系统需要决定如何区分它们的优先级。两个主要因素使这一决定复杂化。第一，冲突需求之间的优先级可能没有完全确定。其次，决策需要在系统内基于学习的组件和环境的非结构化、不可预测和非合作性质所产生的不确定性下做出。因此，建立自治系统的正确性，需要规范语言，捕捉不平等的重要性的要求，量化违反每一个要求，并纳入所面临的不确定性的systems.The拟议的努力目标在理论基础和计算工具的主要差距，使实际应用的正式方法在整个开发过程中的自治系统，包括基于学习的组件，在不确定的环境中运作，并受到与部分确定的优先事项相冲突的要求的影响。其主要新奇在于（1）概率规则手册的开发，这是一种新的规范形式主义，它捕获了不确定性和违反要求程度之间的权衡，并利用这种违反风险以及要求中部分确定的优先级来建立一致的顺序系统轨迹之间的顺序，（2）最小违规控制合成算法，其基于概率规则手册最小化总违规风险并且允许以可证明正确的方式并入基于学习的功能，以及（3）定量验证框架，其利用基于学习的组件和环境的统计分析以及概率规则手册来提供系统的定量分析。这种发展将成为建立自主系统保证的关键一步。该项目将在一个小型自主平台上验证理论成果和算法，该平台也将用于教育目的。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。

项目成果

Formal Methods for Autonomous Systems

自治系统的形式化方法

• DOI: 10.1561/2600000029
• 发表时间: 2023
• 期刊: Foundations and Trends® in Systems and Control
• 作者: Wongpiromsarn, Tichakorn;Ghasemi, Mahsa;Cubuktepe, Murat;Bakirtzis, Georgios;Carr, Steven;Karabag, Mustafa O.;Neary, Cyrus;Gohari, Parham;Topcu, Ufuk
• 通讯作者: Topcu, Ufuk

Evaluation Metrics for Object Detection for Autonomous Systems

• DOI: 10.48550/arxiv.2210.10298
• 发表时间: 2022-10
• 期刊: ArXiv
• 作者: Apurva Badithela;T. Wongpiromsarn;R. Murray

Design and Evaluation of Object Classifiers for Probabilistic Decision-Making in Autonomous Systems

• DOI: 10.1109/icra46639.2022.9812171
• 发表时间: 2022-05
• 期刊: 2022 International Conference on Robotics and Automation (ICRA)
• 作者: Hamad Ullah;Weisi Fan;T. Wongpiromsarn