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CAREER: Robustness Analysis of Uncertain Programs: Theory, Algorithms, and Tools

CAREER: Robustness Analysis of Uncertain Programs: Theory, Algorithms, and Tools
职业:不确定程序的鲁棒性分析:理论、算法和工具
批准号:
1156059
负责人:
Swarat Chaudhuri
金额:
$34.54万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-07-01 至 2017-04-30

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中文摘要
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英文摘要
This research program will develop mathematical techniques to automatically prove the robustness of computer programs operating under uncertainty. The aim is to bridge the gap between control-theoretic studies of robustness, on the one hand, and program semantics and analysis on the other. In cyberphysical systems, where sensor-derived data from the physical world is intertwined with computations, uncertainty arises from volatile or erroneous sensor data. Such systems demand predictable responses to perturbations of the system's initial conditions, otherwise they will be unreliable. The project has three themes: (1) Theory: It will create comprehensive semantics of robustness properties such as continuity and stability in the setting of imperative programs over complex data types. (2) Algorithms: Analysis algorithms are needed to prove robustness of programs, or alternately find violations of robustness. Scalable, lightweight analyses will be developed along with precise but heavyweight methods. (3) Tools: These analyses will be implemented in tools applicable to software for real-world cyber-physical systems.
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SHF: Medium: Neurosymbolic Agents for Formal Theorem-Proving
  • 批准号:
    2403211
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $120.0万
  • 财政年份:
    2024
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  • 依托单位:
Collaborative Research: PPoSS: Large: A Full-stack Approach to Declarative Analytics at Scale
  • 批准号:
    2316161
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $45.0万
  • 财政年份:
    2023
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  • 依托单位:
Collaborative Research: SHF: Medium: Semantics-Aware Neural Models of Code
  • 批准号:
    2212559
  • 项目类别:
    Standard Grant
  • 资助金额:
    $40.0万
  • 财政年份:
    2022
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SHF: Medium: Collaborative Research: Bridging Automated Formal Reasoning and Continuous Optimization for Provably Safe Deep Learning
  • 批准号:
    2033851
  • 项目类别:
    Standard Grant
  • 资助金额:
    $50.0万
  • 财政年份:
    2020
  • 负责人:
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  • 依托单位:
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