Closed · HR001119S0030 · CFDA 12.910 · Discretionary

Competency-Aware Machine Learning (CAML)

Federal grant opportunity posted by DARPA - Defense Sciences Office, cataloged on Grants.gov.

Varies
Award range
Closed
Status
April 22, 2019
Close date
-
Expected awards

The verdict

Competency-Aware Machine Learning (CAML) is a closed discretionary listing from DARPA - Defense Sciences Office that offered Varies by applicant. Future funding cycles may be published under the same CFDA number. PlainGrants indexes 175 other opportunities from DARPA - Defense Sciences Office.

Varies
award range
Closed
application status
12.910
CFDA program
175
other opportunities from DARPA - Defense Sciences Office

Archived opportunity record

Competency-Aware Machine Learning (CAML) is closed (last close April 22, 2019). Check the agency catalog for replacement NOFOs. DARPA - Defense Sciences Office still indexes 175 other opportunities here.

According to Grants.gov (extract vintage 2026-08-10), this record reflects the officially published listing. See methodology.

Deadline tracker

Application window closed

Closed April 22, 2019

Posted Feb 19 Closes Apr 22

Instrument

Procurement Contract

Description

The Defense Sciences Office (DSO) at the Defense Advanced Research Projects Agency (DARPA) is soliciting innovative research proposals in the area of competency-awareness machine learning, whereby an autonomous system can self-assess its task competency and strategy and express both in a human-understandable form. This competency-awareness capability contributes to the goal of transforming autonomous systems from tools into trusted, collaborative partners. The resulting competency-aware machine learning systems will enable machines to control their behaviors to match user expectations and allow human operators to quickly and accurately gain insight into a system’s competence in complex, time-critical, dynamic environments. The Competency-Aware Machine Learning (CAML) program will, in this way, improve the efficiency and effectiveness of human-machine teaming. Proposed research should investigate innovative approaches that enable revolutionary advances in science. DSO will exclude proposals that propose evolutionary improvement to the existing state of practice.

Catch-all / unrestricted eligibility mark · HR001119S0030

Grants.gov marks Competency-Aware Machine Learning (CAML) with a catch-all / unrestricted eligibility code (Others (see official listing text)). Read the official announcement text for the real applicant filter — the code alone is not a door list. The extract does not mark applicant cost-sharing as required. Instrument: Procurement Contract. Funding-activity code maps to Science and Technology / R&D (ST). This eligibility-code string dominates the DARPA - Defense Sciences Office book (77 peers, 44% of 176).

Code 25
Others (see official listing text)

Codes are Grants.gov eligibility category values from the extract vintage 2026-08-10. Confirm the full eligibility statement on the official listing or the Grants.gov eligibility reference.

Official Listing on Grants.gov

View full details, application forms, and submission instructions.

View on Grants.gov

Agency Contact

BAA Coordinator CAML@darpa.mil

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What to do next

Competency-Aware Machine Learning (CAML) is closed; use the parent program and agency catalog for the next funding cycle.

  • Browse open federal opportunities in the same category when this listing is closed. Open opportunities
  • Look up CFDA 12.910 across all indexed listings before you commit to this opportunity alone. Eligibility finder
  • Compare award ranges across federal categories before you pick a single CFDA. All categories

Opportunity deadlines and eligibility change on Grants.gov; PlainGrants mirrors the extract and is not the submission system.

Disclaimer: This information is sourced from Grants.gov and SAM.gov and is for informational purposes only. Opportunity details, deadlines, and eligibility requirements change frequently. Always verify current information directly on Grants.gov before applying. PlainGrants is not affiliated with any federal agency.

Read our methodology - how this data is sourced, computed, and verified.

Data sourced from the SAM.gov Assistance Listings and Grants.gov. See our methodology for details. Retrieved and formatted by PlainGrants