Opportunity Information: Apply for W911NF 17 S 0003 SN MACHINE LEARNING
The Human-Guided Machine Learning opportunity is a Department of Defense (Department of the Army, Army Research Laboratory under Army Materiel Command) research solicitation released as a Special Notice under the ARL Broad Agency Announcement (BAA) W911NF-17-S-0003. It uses a cooperative agreement funding instrument, which generally means the Army expects active collaboration and involvement during the performance of the work rather than a hands-off grant structure. The program sits in the Science and Technology and other Research and Development activity area (CFDA 12.431) and is framed as a discretionary funding opportunity.
The core purpose is to attract research proposals that advance "human-guided machine learning," specifically aligned to two ARL BAA topic areas: KCI-HS-1, Robust Human and Machine Hybridization, and CCE-HS-4, Humans in Multi-Agent Systems. In practical terms, the Army is looking for work that makes machine learning systems more effective when they are shaped, corrected, supervised, or partnered with humans, especially in complex, operationally realistic contexts where multiple autonomous agents and human decision makers interact. The emphasis is not only on improving algorithms, but on developing the broader science and engineering needed for humans and intelligent technologies to function as an integrated team.
The research areas highlighted in the notice include novel forms of human-intelligent technology decision making, cybernetics, and hybridized thinking between people and intelligent systems. This points to interest in approaches where humans provide guidance that is richer than simple labels, such as interactive teaching, intent or preference communication, constraint specification, real-time feedback, and shared control. It also points to building systems that can adapt to human cognitive strengths and limitations, and that can leverage human judgment in uncertain, dynamic environments. Another stated interest is "human technological savvy," which suggests research on how user expertise, training, mental models, and interface design affect outcomes when humans guide or collaborate with machine learning, as well as methods that help users become better supervisors or teammates of AI-enabled systems.
A final explicit focus area is the development of assessment tools for human-intelligent technology teaming. That implies the Army wants rigorous ways to measure and predict team performance, trust, workload, situation awareness, decision quality, and resilience when humans and AI share tasks. In multi-agent settings, this can include evaluating how humans coordinate with swarms or teams of autonomous agents, how information is presented and prioritized, how authority and initiative shift between human and machine, and how the overall system behaves under stress, deception, or degraded communications.
From a funding and schedule standpoint, the opportunity listed an award ceiling of $2,000,000 with an expectation of about three awards. The posting indicates it was created June 10, 2021 with an original closing date of June 25, 2021, signaling a relatively short turnaround typical of special notices tied to an existing BAA. Eligibility is marked as "Others" with additional clarification expected in the full notice, meaning prospective applicants would need to consult the Special Notice attachment and the parent BAA language to confirm who can apply and under what conditions.
Overall, this call targets research that strengthens the scientific foundations and practical implementations of systems where humans actively shape learning and decision processes, and where AI is deployed as a teammate within multi-agent environments. The Army is signaling interest in integrated solutions spanning machine learning, human factors, cognition, interfaces, teaming frameworks, and evaluation methods, with the end goal of robust, measurable, and operationally useful human-machine hybrid performance.Apply for W911NF 17 S 0003 SN MACHINE LEARNING
- The Department of Defense, Dept of the Army -- Materiel Command in the science and technology and other research and development sector is offering a public funding opportunity titled "Human-Guided Machine Learning" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 12.431.
- This funding opportunity was created on Jun 10, 2021.
- Applicants must submit their applications by Jun 25, 2021. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $2,000,000.00 in funding.
- The number of recipients for this funding is limited to 3 candidate(s).
- Eligible applicants include: Others (see text field entitled Additional Information on Eligibility for clarification).
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Human-Guided Machine Learning (ARL BAA Special Notice) - FAQs
1) What is the Human-Guided Machine Learning opportunity?
It is a Department of Defense research solicitation issued by the Department of the Army, Army Research Laboratory (ARL), under Army Materiel Command. It was released as a Special Notice under the ARL Broad Agency Announcement (BAA) W911NF-17-S-0003 and seeks research proposals in "human-guided machine learning."
2) Which Army organization is sponsoring this solicitation?
The sponsoring organization is the U.S. Army Research Laboratory (ARL), operating under Army Materiel Command, within the Department of the Army (DoD).
3) What type of funding instrument will be used?
The notice specifies a cooperative agreement. This typically indicates the Army expects substantial involvement and active collaboration during project execution, rather than a hands-off assistance award.
4) How is a cooperative agreement different from a traditional grant in this context?
Based on the notice language, a cooperative agreement implies ARL anticipates being actively involved during performance of the work (for example, collaboration, coordination, or participation), rather than simply providing funds and receiving periodic reports.
5) What is the CFDA number and activity area associated with this opportunity?
The opportunity is associated with CFDA 12.431 and sits in the Science and Technology and other Research and Development activity area.
6) Is this a discretionary funding opportunity?
Yes. The notice describes it as a discretionary funding opportunity.
7) What is the main goal of the research being sought?
The goal is to advance human-guided machine learning, particularly research that improves how machine learning systems are shaped, corrected, supervised, or partnered with humans in complex and operationally realistic environments, including multi-agent settings.
8) Which BAA topic areas does this Special Notice align to?
The Special Notice is aligned to two ARL BAA topic areas:
- KCI-HS-1: Robust Human and Machine Hybridization
- CCE-HS-4: Humans in Multi-Agent Systems
9) What does "human-guided machine learning" mean in this solicitation?
In practical terms, it refers to machine learning systems that become more effective when humans provide guidance beyond simple labeling. This includes interactive teaching, communication of intent or preferences, specifying constraints, giving real-time feedback, and enabling shared control between humans and intelligent systems.
10) Does the Army want algorithm development only, or broader work?
The emphasis is not only on improving algorithms. The notice highlights interest in broader science and engineering to enable integrated human-intelligent technology teaming, including human factors, cognition, interfaces, teaming frameworks, and assessment methods.
11) What kinds of research areas are highlighted in the Special Notice?
The notice highlights research areas such as:
- Novel forms of human-intelligent technology decision making
- Cybernetics
- Hybridized thinking between people and intelligent systems
12) What types of human input are of interest beyond basic training labels?
The notice points to richer guidance signals such as interactive teaching, intent/preference communication, constraint specification, real-time feedback, and shared control, implying a focus on human inputs that shape learning and decision-making during operation.
13) What does "operationally realistic" imply for proposed research?
It suggests the Army is interested in approaches that work under complex, uncertain, dynamic conditions where multiple autonomous agents and human decision-makers interact, rather than only idealized or purely lab-based scenarios.
14) What is meant by "humans in multi-agent systems" in this call?
It refers to settings where humans coordinate with teams of autonomous agents (for example, multiple agents working together) and where effective interaction depends on information flow, authority/initiative allocation, and the overall behavior of the human-agent team.
15) What is "human technological savvy" and why does it matter here?
The notice flags "human technological savvy" as an interest area, suggesting research on how user expertise, training, mental models, and interface design influence outcomes when humans guide or collaborate with machine learning, as well as methods that help users become more effective supervisors or teammates of AI-enabled systems.
16) Is assessment and evaluation part of the scope?
Yes. A stated focus area is developing assessment tools for human-intelligent technology teaming, indicating a need for rigorous measures and prediction methods for performance and related human-system outcomes.
17) What outcomes might assessment tools measure for human-AI teaming?
The notice implies assessment interests including team performance, trust, workload, situation awareness, decision quality, and resilience in human-intelligent technology teams.
18) What evaluation issues are especially relevant in multi-agent settings?
In multi-agent contexts, the notice suggests evaluating how humans coordinate with agent teams or swarms, how information is presented and prioritized, how authority and initiative shift between human and machine, and how the system behaves under stress, deception, or degraded communications.
19) What is the maximum award amount (award ceiling)?
The listing indicates an award ceiling of $2,000,000.
20) How many awards are anticipated?
The opportunity notes an expectation of about three awards.
21) When was the opportunity posted and when was it originally due?
The posting indicates it was created on June 10, 2021, with an original closing date of June 25, 2021.
22) Does the Special Notice suggest a short proposal turnaround?
Yes. The roughly two-week window between posting and the original closing date suggests a short turnaround typical of special notices issued under an existing BAA.
23) Who is eligible to apply?
Eligibility is marked as "Others," with additional clarification expected in the full notice. Prospective applicants are expected to consult the Special Notice attachment and the parent ARL BAA (W911NF-17-S-0003) language to confirm eligibility and any conditions.
24) What documents should applicants review to confirm requirements and eligibility?
The notice indicates applicants should review the Special Notice attachment and the parent ARL Broad Agency Announcement (BAA) W911NF-17-S-0003 for eligibility details and application conditions.
25) What kinds of integrated solutions does the Army appear to be signaling interest in?
The call signals interest in integrated solutions spanning machine learning, human factors, cognition, interfaces, teaming frameworks, and evaluation methods, with the end goal of robust, measurable, and operationally useful human-machine hybrid performance.
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