NSF award 2548746 · University of Texas at Arlington

Collaborative Research: Scaling Up Robust Multi-Agent Systems: Bridging the Gap between Theoretical Foundations and Practical Applications

Final report due

28 October 2029

in 1,106 days · The award ends on 30 June 2029.

GrantClerk is a private service. We are not NSF or any government agency.

Dates this award owes

NSF emails you about this too.

Decide about an extension2 Mar 2029in 866 days
award ending 2029-06-30 · Research.gov
This one is ours, not NSF’s.
Extension request16 May 2029in 941 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice20 Jun 2029in 976 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Oct 2029in 1106 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Oct 2029in 1106 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Oct 2029in 1106 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

Annual report dates come from the budget period on your award notice. That date is not public, so it is not shown here.

How we got this date

NSF lists this award's end date as 30 June 2029.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 October 2029.

Read the rule on Research.gov

Get a note before each report date

Add this award to your desk. We email you before each date and keep the dates in one place.

Renews each year until you cancel. Cancel any time from your account.

Already a member? Sign in

Award card

Award number: 2548746
Period of support: 15 Jul 2026 – 30 Jun 2029
Amount: $299,963
Awardee: University of Texas at Arlington, Arlington, TX
Program: EPCL: Energy, Power, Control,
Directorate: ENG

Abstract

This NSF project aims to develop efficient learning methods that enable autonomous multi-agent systems to perform effectively and reliably in unpredictable real-world environments. Artificial intelligence (AI) systems trained in simulations often suffer performance degradation or even catastrophic failure due to inevitable mismatches between the idealized training environment and the noisy, variable reality. In…

Is this right?

We copy this from the public NSF award record. Your award notice and NSF always win. If a date looks wrong, check Research.gov.