NSF award 2551666 · University of Southern California

AF: Advancing the Theory of Online Optimization and Learning within Multi-Agent Systems

Final report due

28 November 2030

in 1,502 days · The award ends on 31 July 2030.

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award ending 2030-07-31 · Research.gov
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Extension request16 Jun 2030in 1337 days
award ending 2030-07-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Jul 2030in 1372 days
award ending 2030-07-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Nov 2030in 1502 days
award ending 2030-07-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Nov 2030in 1502 days
award ending 2030-07-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Nov 2030in 1502 days
award ending 2030-07-31 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

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NSF lists this award's end date as 31 July 2030.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 November 2030.

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Award card

Award number: 2551666
Period of support: 1 Aug 2026 – 31 Jul 2030
Amount: $871,225
Awardee: University of Southern California, LOS Angeles, CA
Program: Algorithmic Foundations
Directorate: CSE

Abstract

Artificial intelligence increasingly relies on multiple software agents that learn and make decisions while interacting with one another in settings such as transportation, communications, automated trading, cybersecurity, robotics, and strategic planning. A major challenge in the field is designing learning methods that remain reliable even when other agents behave unpredictably all while enabling the overall…

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