Award Abstract # 2139781
Collaborative Research: CPS: Medium: ASTrA: Automated Synthesis for Trustworthy Autonomous Utility Services

NSF Org: ECCS
Division of Electrical, Communications and Cyber Systems
Recipient: UNIVERSITY OF CALIFORNIA IRVINE
Initial Amendment Date: March 31, 2022
Latest Amendment Date: March 31, 2022
Award Number: 2139781
Award Instrument: Standard Grant
Program Manager: Eyad Abed
eabed@nsf.gov
 (703)292-2303
ECCS
 Division of Electrical, Communications and Cyber Systems
ENG
 Directorate for Engineering
Start Date: April 1, 2022
End Date: March 31, 2025 (Estimated)
Total Intended Award Amount: $335,000.00
Total Awarded Amount to Date: $335,000.00
Funds Obligated to Date: FY 2022 = $335,000.00
History of Investigator:
  • Yasser Shoukry (Principal Investigator)
    yshoukry@uci.edu
Recipient Sponsored Research Office: University of California-Irvine
160 ALDRICH HALL
IRVINE
CA  US  92697-0001
(949)824-7295
Sponsor Congressional District: 47
Primary Place of Performance: University of California-Irvine
Irvine
CA  US  92697-2625
Primary Place of Performance
Congressional District:
47
Unique Entity Identifier (UEI): MJC5FCYQTPE6
Parent UEI: MJC5FCYQTPE6
NSF Program(s): CPS-Cyber-Physical Systems
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7918
Program Element Code(s): 791800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041, 47.070

ABSTRACT

Large-scale systems with societal relevance, such as power generation systems, are increasingly able to leverage new technologies to mitigate their environmental impact, e.g., by harvesting energy from renewable sources. This NSF CPS project aims to investigate methods and computational tools to design a new user-centric paradigm for energy apportionment and distribution and, more broadly, for trustworthy utility services. In this paradigm, distributed networked systems will assist the end users of electricity in scheduling and apportioning their consumption. Further, they will enable local and national utility managers to optimize the use of green energy sources while mitigating the effects of intermittence, promote fairness, equity, and affordability. This project pursues a tractable approach to address the challenges of modeling and designing these large-scale, mixed-autonomy, multi-agent CPSs. The intellectual merits include new scalable methods, algorithms, and tools for the design of distributed decision-making strategies and system architectures that can assist the end users in meeting their goals while guaranteeing compliance with the fairness, reliability, and physical constraints of the design. The broader impacts include enabling the automated design of distributed CPSs that coordinate their decision-making in many applications, from robotic swarms to smart manufacturing and smart cities. The research outcomes will also be used in K-12 and undergraduate STEM outreach efforts.

The proposed framework, termed Automated Synthesis for Trustworthy Autonomous Utility Services (ASTrA), addresses the design challenges via a three-pronged approach. It uses population games to model the effect of distributed decision-making infrastructures (DMI) on large populations of strategic agents. DMIs will be realized via dedicated networked hybrid hardware architectures and algorithms we seek to design. ASTrA further introduces a systematic, layered methodology to automate the design, verification, and validation of DMIs from expressive representations of the requirements. Finally, it offers a set of cutting-edge computational tools to facilitate our methodology by enabling efficient reasoning about the interaction between discrete models, e.g., used to describe complex missions or embedded software components, and continuous models used to describe physical processes. The evaluation plan involves experimentation on a real testbed designed for zero-net-energy applications.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Fatnassi, Wael and Khedr, Haitham and Yamamoto, Valen and Shoukry, Yasser "BERN-NN: Tight Bound Propagation For Neural Networks Using Bernstein Polynomial Interval Arithmetic" Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control (HSCC 2023) , 2023 https://doi.org/10.1145/3575870.3587126 Citation Details
Fatnassi, Wael and Shoukry, Yasser "PolyARBerNN: A Neural Network Guided Solver and Optimizer for Bounded Polynomial Inequalities" ACM Transactions on Embedded Computing Systems , v.23 , 2024 https://doi.org/10.1145/3632970 Citation Details
Ferlez, James and "Polynomial-Time Reachability for LTI Systems With Two-Level Lattice Neural Network Controllers" IEEE control systems letters , v.7 , 2022 https://doi.org/10.1109/LCSYS.2022.3231556 Citation Details
Khedr, Haitham and Shoukry, Yasser "CertiFair: A Framework for Certified Global Fairness of Neural Networks" Proceedings of the AAAI Conference on Artificial Intelligence , v.37 , 2023 https://doi.org/10.1609/aaai.v37i7.25994 Citation Details
Khedr, Haitham and Shoukry, Yasser "DeepBern-Nets: Taming the Complexity of Certifying Neural Networks Using Bernstein Polynomial Activations and Precise Bound Propagation" Proceedings of the AAAI Conference on Artificial Intelligence , v.38 , 2024 https://doi.org/10.1609/aaai.v38i19.30117 Citation Details
Odema, Mohanad and Ferlez, James and Shoukry, Yasser and Al Faruque, Mohammad Abdullah "SEO: Safety-Aware Energy Optimization Framework for Multi-Sensor Neural Controllers at the Edge" , 2023 https://doi.org/10.1109/DAC56929.2023.10247751 Citation Details
Odema, Mohanad and Ferlez, James and Vaisi, Goli and Shoukry, Yasser and Al Faruque, Mohammad Abdullah "EnergyShield: Provably-Safe Offloading of Neural Network Controllers for Energy Efficiency" , 2023 https://doi.org/10.1145/3576841.3585935 Citation Details
Sun, Xiaowu and Shoukry, Yasser "Neurosymbolic Motion and Task Planning for Linear Temporal Logic Tasks" IEEE Transactions on Robotics , v.40 , 2024 https://doi.org/10.1109/TRO.2024.3392079 Citation Details
Sun, Xiaowu and Shoukry, Yasser "NNSynth: Neural Network Guided Abstraction-Based Controller Synthesis for Stochastic Systems" 2022 IEEE 61st Conference on Decision and Control (CDC) , 2022 https://doi.org/10.1109/CDC51059.2022.9993158 Citation Details

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