TY - GEN
T1 - Stochastic stackelberg games for agent-driven robust design
AU - Rismiller, Sean C.
AU - Cagan, Jonathan
AU - McComb, Christopher
N1 - Funding Information:
This work was supported by the Defense Advanced Research Projects Agency under Cooperative Agreement N66001-17-2-4064. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the view of the sponsor.
Publisher Copyright:
© 2020 American Society of Mechanical Engineers (ASME). All rights reserved.
PY - 2020
Y1 - 2020
N2 - Products must often endure unpredictable and challenging conditions while fulfilling their intended functions. Gametheoretic methods make it possible for designers to design solutions that are robust against complicated conditions, however, these methods are often specific to the problems they investigate. This work introduces the Game-Augmented Robust Simulated Annealing Teams (GARSAT) framework, a gametheoretic agent-based architecture that generates solutions robust to variation, and models problems with elementary information, making it easily extendable. The platform was used to generate designs under consideration of a multidimensional attack. Designs were produced under various adversarial settings and compared to designs generated without considering adversaries to validate the model. The process successfully created robust designs able to withstand multiple combined conditions, and the effects of the adversarial settings on the designs were explored.
AB - Products must often endure unpredictable and challenging conditions while fulfilling their intended functions. Gametheoretic methods make it possible for designers to design solutions that are robust against complicated conditions, however, these methods are often specific to the problems they investigate. This work introduces the Game-Augmented Robust Simulated Annealing Teams (GARSAT) framework, a gametheoretic agent-based architecture that generates solutions robust to variation, and models problems with elementary information, making it easily extendable. The platform was used to generate designs under consideration of a multidimensional attack. Designs were produced under various adversarial settings and compared to designs generated without considering adversaries to validate the model. The process successfully created robust designs able to withstand multiple combined conditions, and the effects of the adversarial settings on the designs were explored.
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U2 - 10.1115/DETC2020-22153
DO - 10.1115/DETC2020-22153
M3 - Conference contribution
AN - SCOPUS:85096329108
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 46th Design Automation Conference (DAC)
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2020
Y2 - 17 August 2020 through 19 August 2020
ER -