STRONG FOA Cycle_6_Final.pdf

UNCLASSIFIED/Approved for Public Release/Distribution Unlimited

ARL Strengthening Teamwork for Robust Operations in Novel Groups (STRONG)

W911NF-19-S-0001

Amendment #9

The purpose of this modification is to issue Cycle 6 of the ARL STRONG Program. This modification includes the topic updates as well as administrative changes to the FOA. Please be sure to read the modification to its entirety. Proposals are due in grants.gov on 5 February 2024. An email receipt will be provided to each Applicant for each Proposal submission received. Applications submitted after the closing date and time will not be considered or evaluated by the Government

FUNDING OPPORTUNITY OVERVIEW 3

A. PROGRAM DESCRIPTION 4

1. Background 4

2. Cycle Structure 5

3. Deliverables 6

4. Cycle 6 Updates 7

B. FEDERAL AWARD INFORMATION 11

C. ELIGIBILITY INFORMATION 13

D. APPLICATION AND SUBMISSION INFORMATION 13

E. APPLICATION REVIEW/EVALUATION INFORMATION 25

F. AWARD ADMINISTRATION INFORMATION 29

G. AGENCY CONTACTS 29

FUNDING OPPORTUNITY OVERVIEW

1. Federal Awarding Agency Name

U.S. Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD 20783-1197

Issuing Acquisition Office U.S. Army Contracting Command – Aberdeen Proving Ground, Research Triangle Park (RTP) Division, 800 Park Office Drive, Suite #4229, Research Triangle Park, NC 27709

2. Research Opportunity Title

Strengthening Teamwork for Robust Operations in Novel Groups (STRONG)

3. Announcement Type Amendment: Cycle 6 updates - SEE SECTION A.4

4. Funding Opportunity Number

W911NF-19-S-0001 (The same FOA number from the first cycle will be used throughout all cycles of the program, but will be amended at the beginning of each cycle to topic area descriptions and other relevant program updates.)

5. Catalog of Federal Domestic Assistance (CFDA) Number(s)

12.630 - "Basic, Applied, and Advanced Research in Science and Engineering"

6. STRONG Website: https://www.arl.army.mil/strong

7. Key Dates:

The following is a summary of the events and dates associated with this Cycle under the overall STRONG Funding Opportunity Announcement (FOA):

EVENT ESTIMATED DATE/TIMEFRAME

Opportunity released December 2023 Deadline for Questions 10 January 2023 Opportunity Webinar 16 January 2024 Proposals due for Cycle 6 5 February 2024 Cycle 6 Awards June 2024 (Expected) Cycle 6 Innovation Summit June 2024 Cycle 6 Seedling Collaboration Meeting August 2024 Cycle 6 Follow On Presentations January 2025

A. PROGRAM DESCRIPTION

Overview: The 10-year STRONG program has a specific long-term goal to identify and implement the fundamental research necessary to develop individualized, adaptive technologies that promote effective teamwork in novel groups of humans and intelligent agents. Success in future militarily scenarios will require faster than human ideation and decision-making within increasingly complex, dynamic, and rapidly evolving sociotechnical contexts. The STRONG Cycle #6 program's research initiatives center on enhancing human-AI synergy within intricate military contexts. The focus areas include developing scalable human-guided machine learning, emphasizing the integration of human intuition into long-term planning algorithms, advancing AI's capability to navigate varied information hierarchies and abstraction levels, and fostering mutual understanding and trust between humans and AI. Collectively, these efforts aim to create a cohesive, efficient, and adaptable collaborative framework to address the evolving challenges of modern military scenarios.

1. Background

The future vision for the U.S. Army includes teams of humans and intelligent agents working together to accomplish missions. The U.S. Army Research Laboratory (ARL) has established a 10-year (FY19- FY29) collaborative program, Strengthening Teamwork for Robust Operations in Novel Groups (STRONG), structured under eight annual program cycles (i.e., Cycles 1-8; FY19-FY26) with the overall goal of developing the foundation for enhanced teamwork within heterogeneous human-intelligent agent teams. This collaborative venture will bring together diverse, multidisciplinary expertise to support scientific breakthroughs relevant to specific and critical scientific questions that must be addressed to enable this future vision.

STRONG focuses directly on coordination and cooperation in human-agent teams via individualized and adaptive systems. The 10-year program has a specific long-term goal to identify and implement the fundamental research necessary to develop individualized, adaptive technologies that promote effective teamwork in novel groups of humans and intelligent agents. Prior to submitting, Applicants should review Decostanza et al. (2018) Enhancing Human-Agent Teaming with Individualized, Adaptive Technologies:

A Discussion of Critical Scientific Questions (https://brain.ieee.org/brain-storm/enhancing-human-agent-teaming/), which provides the initial vision and discussion of the scientific questions underlying the STRONG program, as well as Metcalf et. al. (2021) Systemic Oversimplification Limits the Potential for Human-AI Partnership which describes re-envisioning human machine teaming paradigms to enable success in problem spaces that are un-accessible to either humans or machine intelligence alone (https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9425540).

Collaboration: STRONG addresses a critical objective within a broader Army goal to enable effective integration of Artificial Intelligence / Machine Learning (AI/ML) on the battlefield. This program has been developed in coordination with other related ARL-funded collaborative efforts (see descriptions of ARL collaborative alliances at https://www.arl.army.mil/www/default.cfm?page=93) and shares a common vision of highly collaborative academia-industry-government partnerships. However, with the rapid pace of technological change and the scientific community’s nascent understanding of and empirical evidence for human-agent teaming, STRONG will implement a novel, synergistic approach/structure. Annual Cycles will be explicitly structured to foster the greatest degree of collaboration and knowledge sharing to optimally leverage or combine findings from each effort awarded under each cycle and directly tie with internal ARL research programs. This structure is intended to more effectively and efficiently achieve the overall goals of the STRONG program by preventing unnecessary duplication or silos of information, findings, or other relevant outcomes from ARL investments (internal and external). In other words, the success of STRONG requires a multidisciplinary, collaborative effort that synergistically views every new funded effort as a network of research or “building block” to grow and support an ecosystem of high-quality, innovative researchers actively sharing knowledge and collaboratively addressing scientific gaps critical to the Department of Defense. Given this novel approach, Government researchers and program managers will be taking a more active role to support rapid integration of findings and cohesion within the research ecosystem.

Support Infrastructure: Each Cycle will require Recipient participation in an annual Innovation Summit Series to foster active collaboration, as detailed below. In addition to general updates, a dedicated STRONG website (https://www.arl.army.mil/strong) will maintain a regularly updated knowledge bank of ARL research priorities, programs and contacts; previously funded research summaries, products and contacts; and other relevant information specific to STRONG. Additional information about the annual Innovation Summit Series is available upon request via email to strong-cra@army.mil .

2. Cycle Structure

STRONG will be executed through a series of eight annual program cycles (i.e., Cycles 1-8; FY19- FY26) with the following format: (1) 12 month seedling awards structured as individual cooperative agreements to each awardee; (2) required awardee participation in the annual Innovation Summit Series to foster novel collaborations; and (3) potential 3-year extension of a seedling award executed as an Option under the original cooperative agreement.

Enhanced Research Program funding from ARL or Other Government Agencies (OGAs) may become available during a cycle which provides a mechanism for growth and enhancement within STRONG. A proposal should not include any discussion of the Enhanced Research Program. Recipients receiving a CA will be notified and provided details if the opportunity for Enhanced Research Program funding becomes available during their award period of performance.

a) Annual Cycle Seedling Announcements:

The FOA will be amended annually with “Cycle updates” to identify a new problem statement, topic, and other necessary changes for that specific Cycle.

o SEE SECTION A.4.a FOR THIS YEAR’S CYCLE TOPIC

Each annual Cycle will begin with a call for “seedling” proposals with funding provided to those Recipients selected under a cooperative agreement (CA).

Total number of seedling Recipients and funding per Recipient may vary from year to year at the discretion of the Government based on annual Cycle changes or requirements.

Each Cycle topic will be focused on addressing a different scientific area within the scope of the broad research aims of STRONG, with all Cycles designed to ultimately converge on the specific long-term program goal noted above.

Cycle topics will be carefully chosen based on both program achievements from the previous year and on scientific advancements by the broader research community.

Prior to the final submission date of each Cycle (see Event Timeline above), ARL will host an opportunity webinar on to provide detailed information and question-answer opportunities. A link to the annual webinar will be posted on the STRONG Program website at https://www.arl.army.mil/strong and can also be obtained via email to strong-cra@army.mil.

Publications and seedling abstracts from each awardee under previously completed Cycles will be shared on https://www.arl.army.mil/strong for all future applicants to review.

Proposals from junior investigators (e.g., students, research fellows, and early-career researchers with less than 5 years past reception of their PhD or less than 5 years’ experience within the primary field of their organization) are appropriate under this opportunity.

b) Innovation Summit Series:

Seedling Recipient participation by at least one team member in each meeting of the

Innovation Summit Series under the Recipient’s awarded Cycle will be REQUIRED.

o SEE SECTION A.4.b FOR THIS YEAR’S TENTATIVE AGENDA

Seedling presentation: Recipients will be expected to demonstrate their scientific principles and approach to the community, providing evidence of progress toward scientific breakthroughs that support the criticality of the particular concept or approach to the specific topic.

Active collaboration: Recipients will be expected to coordinate their future research plans with complimentary ongoing or planned efforts executed by other Recipients and government researchers within their designated Cycle. Regular communication between seedling Recipients and government researchers to develop awareness of relevant ARL-internal research programs is encouraged prior to, during, and after the Innovation Summit Series.

Connections to broader ARL programs, other service labs or other Government agencies is also encouraged. Lastly, novel connections to and collaborations with the commercial sector is strongly encouraged.

Multi-year Extension Option: Recipients that wish to apply for up to a 3-year Extension Option will be expected to present their proposed project to an evaluation panel at a virtual/in-person hybrid event that will be scheduled in January 2025.

c) Multi-year Extension Option:

Recipients of a seedling CA under the annual Cycle are eligible for consideration to receive funding (see Table 1) for an optional extension of up to 3 years at the conclusion of the seedling award. The period of the performance of the option will be based on the research and available funding.

In addition to active collaboration during the Innovation Summit Series as noted above, explicit reference to any connection, extension potential, or refute of prior Cycle efforts is expected, where applicable. In other words, demonstrable knowledge of prior and current STRONG efforts is expected.

3. Deliverables / Research Products

Given the cyclical and collaborative nature of STRONG, awardees are encouraged to share research products (e.g., databases and algorithms/code) whenever possible with ARL and fellow awardees in order to contribute to the collective S&T advancement of the STRONG program and the human-agent teaming research community and ecosystem. Creative ideas to enable enduring and multiplicative benefits (e.g., government and commercial) of the research conducted under STRONG is highly encouraged. Additionally, the intent is to leverage past work to promote greater return on investment (ROI) for government investments under each award. ARL will maintain a central repository for all such STRONG deliverables, and as appropriate, these deliverables will be shared on the STRONG website.

a) Required seedling deliverables:

Prior to 1st Summit meeting: Seedling Research Summary/Plan (publicly releasable, 1/2pg target)

End of seedling award: Final Seedling Research Report (publicly releasable, 1-2pg max)

b) Required extension deliverables:

1 month post modification notification: Proposed Research Summary/Abstract (public releasable, 1/2pg target)

Annually for extension period of performance: Annual Research Report & Summit presentation (public releasable, 1-2pg max)

c) Additional project-specific deliverable examples are below and will be determined at award time:

Annotated databases Algorithms developed/used Testbed description, including access if possible Any other relevant research product

Cycle 6: Scalable Interactive Machine Learning and Planning

Future warfare will require Command and Control (C2) personnel to make decisions at shrinking timescales in increasingly-complex scenarios in multi-domain operations, requiring coordination across multiple dispersed nodes, forces, and autonomous agents while maintaining resiliency to enemy actions.

Current approaches to C2 are time-consuming, linear, and consisting of many steps, and therefore may fail to maintain decision-making superiority on the future battlefield. This call seeks to enable the ability to leverage the strengths of both people and artificial intelligence (AI) to change the nature of future command and control. Cycle #6 of the STRONG CRA focuses on four critical research areas for future command and control: a) scaling human-guided machine; b) human in the loop dynamic planning for long time horizon tasks; and c) learning across multiple hierarchies and levels of abstraction, and d) adapting human-AI collaborations to teams with diverse KSBs and roles. Basic research conducted under this call will develop scalable interactive machine learning methods that will be critical for adapting future C2 operations in the Army.

The age of AI has ushered in a wave of transformative technologies, with the potential to revolutionize numerous sectors, including the military. As these technologies evolve, the symbiotic relationship between humans and AI has become a focal point of research. The balance of this relationship is crucial: while AI provides computational prowess, pattern recognition, and rapid data processing, humans bring intuition, contextual understanding, and ethical considerations to the table.

One core challenge in this domain is scalability. In complex operations, such as those encountered in military contexts, the integration of AI is not a simple one-to-one relationship. Operations often require multiple AI systems to collaborate with a diverse group of humans, each bringing their unique Knowledge, Skills, and Behaviors (KSBs) to the situation. Designing AI systems that can adapt and respond to varying human expertise levels is paramount for the success of such collaborations.

Another significant aspect is the dynamic nature of planning in military operations. While AI systems excel in processing vast amounts of data and drawing insights, human intuition plays a pivotal role, especially when planning spans across different time horizons. The challenge lies in designing AI systems that can seamlessly merge immediate decision-making with long-term strategic planning while accommodating human insights.

Furthermore, the hierarchical organization of information is inherent in many military and strategic scenarios. AI systems must be adept at transitioning between different levels of abstraction, from macro-level strategic views to micro-level tactical insights. This versatility not only ensures efficient problem-solving but also enables the AI to cater to diverse challenges of varying complexities.

Lastly, the cornerstone of effective human-AI collaboration is mutual understanding. Building trust between humans and AI is not merely about transparency in AI's decision-making processes but also about ensuring that AI systems can interpret and act upon the nuanced intentions of their human collaborators.

In light of these challenges, the STRONG program's Cycle 6 aims to address the pressing questions in the field of human-AI collaboration, by diving deep into the realm of Scalable Interactive Machine Learning, aiming to forge a transformative synergy between human and machine intelligence. This initiative seeks to harness the combined strengths of both entities, enabling them to collaboratively think, ideate, predict, evaluate, decide, adapt and plan together. The overarching goal enable the future of army MDMP and command and control (C2) by transcending the individual capabilities of bot humans and machines, especially in dynamic and intricate environments, ushering in a new era of enhanced collaborative intelligence tailored for multifaceted challenges.

Cycle 6 Goals and Questions:

Cycle 6 “Seedlings” must address one question from questions Q1 to Q4 in bold below, 1 from each of the research areas A to D. For Cycle 6 “Multi-Year Options”, it is expected that proposals will address at least two of the four questions Q1 to Q4 below from research areas A to D. , to support the multidisciplinary, collaborative “building block” approach that is a hallmark of the STRONG program."

a) Scaling Interactive Machine Learning

The objective of this research area is to facilitate machine learning processes that are influenced and guided by human expertise. A central challenge to address is scalability. In various complex scenarios, especially those common in military contexts, relying on a single AI system or one human expert might not be adequate. A more expansive approach would involve integrating the expertise and strengths of multiple humans and AI systems. The expected outcome of this research endeavor is to produce a framework wherein the collective intelligence of both humans and machines can efficiently collaborate and function at a larger scale.

Q1) How can we design AI systems and machine learning processes that integrate information and are guided by the expertise of multiple humans, ensuring scalability and efficiency in collaborative human-AI environments?

Examples:

Balancing Inputs in Small Teams: In a scenario with two humans and multiple AI systems, determining how to weigh and prioritize human input can be challenging. For example, if the two humans have conflicting viewpoints, how should the AI reconcile these differences while still leveraging its own data-driven insights?

Managing Opinions in Larger Teams: When scaling to a team of 10 or more humans, the complexity increases. With more human opinions and perspectives, how can the AI system ensure that every member's expertise is considered without overwhelming the decision-making process?

Integration of Multiple Viewpoints: In multi-human, multi-AI systems, striking a balance between human inputs and AI-driven insights becomes pivotal. The system must be adept at merging diverse human opinions, potentially conflicting, with the analytical prowess of AI, ensuring the collective decision is optimal and representative.

b) Human-in-the-Loop Dynamic Planning for Long Time Horizon Tasks

This research component focuses on tasks that require foresight and extend over prolonged durations. In military and strategic scenarios, certain tasks demand a unique blend of immediate decision-making and long-term planning. Here, the value of human intuition and foresight becomes paramount. The goal is to create algorithms that can seamlessly incorporate human expertise into the planning process. By doing so, the resulting decisions and strategies are not only data-driven but also benefit from human intuition, making them more holistic and effective in the long run.

Q2) How can we develop algorithms that efficiently merge short-term decision-making with long-term planning, ensuring that both AI knowledge and human intuition and contextual understanding is seamlessly incorporated, especially in dynamic scenarios with competing goals spanning multiple time horizons?

Examples:

Competing Goals: In military scenarios, there may often be immediate objectives that conflict with long-term strategic goals. Designing an algorithm that can navigate these competing priorities while integrating human intuition is crucial. For instance, how can the Human-AI system decide when to prioritize a short-term tactical advantage over a longer-term strategic benefit?

Handling Multi-Time Horizon Tasks: Military operations often span various timeframes, from immediate actions to strategies that play out over years. Balancing the need for quick responses with the foresight required for future planning is essential. How can the human-AI system adjust its recommendations based on the urgency of the task while still considering long-term implications?

Incorporation of Human Expertise: While AI can process vast amounts of data rapidly, human intuition, especially in complex military contexts, is invaluable. The challenge lies in designing a system where the AI not only considers data-driven insights but also gives appropriate weight to human expertise, ensuring that the combined decision is both timely and informed.

c) Learning Across Multiple Hierarchies and Levels of Abstraction

The ambition here is to empower AI systems to understand, learn, and operate across diverse levels of information hierarchy and abstraction. Problems and challenges vary in complexity, and it's essential for AI systems to adapt accordingly. Some issues demand a broader, macro-level perspective, while others require a detailed, micro-level analysis. An AI capable of transitioning and learning across these levels offers enhanced versatility. Such a capability will lead to improved problem-solving and decision-making skills in AI, achieved through a more structured and hierarchical organization of information and knowledge.

Q3) How can we design AI systems that seamlessly transition and adapt across varying levels of information hierarchy and abstraction, ensuring effective problem-solving and decision-making across a spectrum of complex challenges?

Examples:

Adaptive Learning Across Hierarchies: Military scenarios present problems of varied complexities. An AI system needs to be adept at shifting its focus from high-level strategic views to granular, tactical insights as required. How can we ensure that the AI system maintains its efficiency while transitioning between these diverse levels?

Balancing Macro and Micro Perspectives: While some situations demand a broad overview, others might need a deep dive into specific details. The challenge lies in ensuring that the AI system can discern which approach is optimal for a given situation and adjust its analytical focus accordingly.

Structured Organization of Information: As the AI operates across different levels of abstraction, it's imperative that the information remains well-organized. How can the system maintain a clear hierarchical structure of knowledge, ensuring that insights derived at any level are based on a solid foundation of underlying data and information?

d) Adapting Human-AI Collaborations to Teams with Diverse KSBs and Roles

Future large scale teams of humans and AI systems will contain human operators with a variety of individual capabilities, skills and background knowledge engaged in performing specific roles within the larger team.

How do we create large scale systems that allow for individualization while maintaining high levels of performance across the entire system. How do these systems take into account differences in individual knowledge, their roles and the roles of individual systems within the overall ecosystem.

Q4) How can we design and adapt human-AI systems to effectively collaborate with teams comprised of members with diverse Knowledge, Skills, Behaviors (KSBs), and varied roles, ensuring optimal performance across different team compositions in military contexts?

Examples:

Tailoring AI Responses to Diverse KSBs: Different team members bring unique knowledge, skills, and behaviors to the table. The AI system needs to be versatile enough to recognize these variations and tailor its interactions and recommendations accordingly. For instance, how can the AI system provide more detailed explanations to a novice while offering advanced strategic insights to an expert?

Role-Based AI Interactions: In a team with varied roles, from strategists to field operatives, the AI must adapt its interactions based on the specific role of the interacting human member. How can the system ensure it provides relevant and role-specific information without overwhelming or under-informing the user?

Dynamic Team Compositions: Military teams can have fluid compositions, with members joining, leaving, or changing roles. The AI system should be adept at quickly recognizing these changes and adjusting its collaboration strategies. What methodologies can be employed to ensure the AI remains effective across different team configurations?

Balancing Collective and Individual Needs: While AI algorithms should cater to individual KSBs and roles, it should also maintain a holistic view to serve the team's collective goals. How can the system strike a balance between individualized interactions and overarching team objectives?

Building Trust Across Diverse Team Members: Trust-building strategies might differ based on the KSBs and roles of team members. How can the AI ensure it builds and maintains trust uniformly across a team with diverse backgrounds and responsibilities?

Implementation and Expected Outcomes

To realize the goals of Cycle 6, the STRONG program plans to bring together a diverse group of experts from fields like machine learning, human cognition, and behavioral science. The collaborative effort will be rooted in extensive experimentation, prototyping, and rigorous evaluation. The vision is to produce tangible deliverables that can be implemented in real-world scenarios. These include scalable frameworks for human- AI collaboration, innovative algorithms for long-term planning, state-of-the-art techniques for hierarchical learning, methods that enhance mutual understanding between humans and AI and Adapting Human-AI Collaborations to Teams with Diverse KSBs and Roles

Conclusion

Cycle 6 of the STRONG program is a visionary initiative that recognizes the growing importance of collaborative efforts between humans and AI. By addressing foundational challenges like scalability, long-term planning, hierarchical learning, and mutual understanding, the program sets the stage for a future where human and machine intelligence can work in tandem, effectively tackling intricate tasks in ever-evolving environments.

a) Innovation Summit Series

The following description is intended to provide a general overview of this Cycle’s activities for planning purposes, but it does not constitute a finalized plan by the Government. The official Innovation Summit Series agenda will be announced to Recipients when finalized. Potential activities may include:

teaching seminars mixing academic, industry, and Army perspectives, focused on innovative practices and successful transition (e.g. military, business start-up); Army science planning and strategy development; Hack-a-thons with quick-solution challenges and mixed teams; brainstorming of research applications and development of transitions; or development of scientific demonstrations and an Industry Day (a day of demonstrations targeted toward industry stakeholders designed to develop multiple transition pathways). Summit activities are intended to foster active collaboration with ARL researchers and other members of the research ecosystem with the specific aim to encourage collaborative planning and development of follow-on (Option) proposals.

Given the current uncertainty related to COVID-19, the summit dates and structure (e.g., in-person, virtual, hybrid) will be determined at a later date, with ample notice given to seedling Recipients and other participants. Fortunately, we have had success with a full in-person summit (Cycle 1, 2019) and two full virtual summits (Cycles 2 and 3, 2020, and 2021), as well as a mixed in person/virtual meeting in 2022.

Notional Activity Outline occurring in the May-August 2024 timeframe:

ORIENTATION & OVERVIEWS - (PIs or representative required)

Introduction to Army science mission, planning and strategy Demonstrations of initial concept and approach from current Cycle Seedling Awardees Presentations from prior Cycle Option Awardees as appropriate Industry Perspective: presentations and brainstorming on shared research challenges and gaps impacting both military and commercial sectors. Identify unique collaboration opportunities and/or multiple paths for research transition

COLLABORATION / TEAM-BUILDING & RESEARCH – (junior/student participation required with guidance by senior/PI)

Facilitated activities to support new collaborations and new ideas for those interested in the Option effort

Opportunity for development of junior investigators to link into the ecosystem of researchers working on questions relative to Army priorities

Hack-a-thon focused on experimental paradigms

Dedicated time to interact with and discuss collaboration options with ARL scientists and specific ARL research programs Independent break-out sessions for research planning

Notional Activity Outline occurring in the January 2025 timeframe:

PROPOSED FOLLOW ON OPTION EFFORTS

Presentation of proposed follow on efforts (approximately 1 hour with questions) open to collaborators and government only.

White papers for Follow-On efforts due.

C. References

Akata, Z., Balliet, D., de Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H., Hung, H., Jonker, C., Monz, C., Neerincx, M., Oliehoek, F., Prakken, H., Schlobach, S., van der Gaag, L., van Harmelen, F., … Welling, M. (2020). A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence. Computer, 53(8), 18–28.

https://doi.org/10.1109/MC.2020.2996587

Bell, B. S., & Kozlowski, S. W. (2002). A typology of virtual teams: Implications for effective leadership. Group & organization management, 27(1), 14-49.

Cangelosi, A., & Invitto, S. (2017). Human-Robot Interaction and Neuroprosthetics: A review of new technologies. IEEE Consumer Electronics Magazine, 6(3), 24–33.

https://doi.org/10.1109/MCE.2016.2614423

DeCostanza, A., Marathe, A., Bohannon, A., Evans, A., Palazzolo, E., Metcalf, J., McDowell, K.

(2018) Enhancing Human-Agent Teaming with Individualized, Adaptive Technologies: A Discussion of Critical Scientific Questions in IEEE Brainstorming

Edwards, G., Contò, F., Bucci, L. K., & Battelli, L. (2020). Controlling Brain State Prior to Stimulation of Parietal Cortex Prevents Deterioration of Sustained Attention. Cerebral Cortex Communications, 1(1), tgaa069. https://doi.org/10.1093/texcom/tgaa069

Ereira, S., Hauser, T. U., Moran, R., Story, G. W., Dolan, R. J., & Kurth-Nelson, Z. (2020). Social training reconfigures prediction errors to shape Self-Other boundaries. Nature Communications, 11(1), Article 1. https://doi.org/10.1038/s41467-020-16856-8

Gil, Y., Honaker, J., Gupta, S., Ma, Y., D’Orazio, V., Garijo, D., Gadewar, S., Yang, Q., &

Jahanshad, N. (2019). Towards human-guided machine learning. Proceedings of the 24th International Conference on Intelligent User Interfaces, 614–624.

https://doi.org/10.1145/3301275.3302324

Goecks, V. G., Gremillion, G. M., Lawhern, V. J., Valasek, J., & Waytowich, N. R. (2019).

Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), Article 01. https://doi.org/10.1609/aaai.v33i01.33012462

Hernández-Orallo, J., & Vold, K. (2019). AI Extenders: The Ethical and Societal Implications of

Humans Cognitively Extended by AI. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 507–513. https://doi.org/10.1145/3306618.3314238

Huffaker, J. S., Kummerfeld, J. K., Lasecki, W. S., & Ackerman, M. S. (2020). Crowdsourced

Detection of Emotionally Manipulative Language. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3313831.3376375

Pollard, K. A., Files, B. T., Oiknine, A. H., & Dalangin, B. (2022). How to prepare for rapidly evolving technology: Focus on adaptability. (ARL-TR-9432). DEVCOM Army Research Laboratory.

Metcalfe, J., Perelman, B., Boothe, D., McDowell, K., ‘Systemic Oversimplification Limits the Potential for Human-AI Partnership’ IEEE Access, May, (2021)

Ramot, M., & Martin, A. (2022). Closed-loop neuromodulation for studying spontaneous activity and causality. Trends in Cognitive Sciences, 26(4), 290–299.

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Systems from Human Interaction (arXiv:1808.09572). arXiv.

https://doi.org/10.48550/arXiv.1808.09572

B. FEDERAL AWARD INFORMATION

Multiple CAs will be awarded from this FOA. The Applicants selected for award will be notified by the Grants Officer or his/her designee telephonically or via email. The CA award is not official until the Recipient has received the award signed by the Grants Officer.

Cooperative agreements for institutions of higher education, nonprofit organizations, foreign organizations, and foreign public entities are primarily governed by the following:

Federal statutes Federal regulations 2 CFR Part 200 2 CFR Part 1104 32 CFR Parts 21, 22, 26, and 28 DoD Research and Development General Terms and Conditions Agency-specific Research Terms and Conditions

Cooperative agreements for for-profit and nonprofit organizations exempted from Subpart E—Cost Principles of 2 CFR Part 200, are primarily governed by the following:

Federal statutes Federal regulations 32 CFR Part 34 – Administrative Requirements for Grants and Agreements with For-Profit

Organizations 32 CFR Parts 21, 22, 26, and 28 DoD Research and Development General Terms and Conditions Agency-specific Research Terms and Conditions

The following websites may be accessed to obtain an electronic copy of the governing regulations and guidance:

FAR, DFARS, and AFARS: https://www.acquisition.gov/ Code of Federal Regulations: http://www.ecfr.gov DoD Research and Development General Terms and Conditions September 2021 ACC-APG-RTP Division Assistance, Research General Terms and

Conditions dated January 2022

Core Funding Cycles:

FY19

FY20

FY21

FY22

FY23

FY24

FY25

FY26

FY27

FY28

FY29

Proposal Submissions

Cycle 1 B O O O DEC 2018

Cycle 2 B O O O SEP 2019

Cycle 3 B O O O SEP 2020

Cycle 5 B O O O SEP 2021

Cycle 5 B O O O SEP 2022

Cycle 6 B O O O SEP 2023

Cycle 7 B O O O SEP 2024

Cycle 8 B O O O SEP 2025

Note: B is for Base (“seedling”) period of Cooperative Agreement; O indicates 3-yr Option.

Funding for seedling project efforts will not exceed $100,000 per award. Award of approximately 10 seedling CAs is expected during this Cycle. Funding for a Multi-year Option beyond the seedling CA is expected to be larger in scope, with an estimated budget ranging between $350,000 and $500,000 per year, per award. Multiple seedling Recipients are encouraged to collaboratively propose an extension effort. Seedling Recipients may include new entities as subawardees that were not part of the initial seedling under a proposed 3-year Option. Any discussion about options is only applicable for those Seedling Recipients who receive an option.

Enhanced Research Program funding from ARL or Other Government Agencies (OGAs) may become available during a cycle which provides a mechanism for growth and enhancement within STRONG. A proposal should not include any discussion of the Enhanced Research Program. Recipients receiving a CA will be notified and provided details if the opportunity for Enhanced Research Program funding becomes available during their award period of performance.

Award Instrument: This Funding Opportunity is expected to result in the award of multiple “seedling” cooperative agreements (CA) during each Cycle as defined at 31 U.S.C. 6305 for the execution of the program. The CA is used to enter into a relationship:

a. The principal purpose of which is to transfer a thing of value to the Recipient to carry out a public purpose of support or stimulation authorized by a law or the United States, rather than to acquire property or services for the Federal Government’s direct benefit or use.

b. Substantial involvement is expected between the Federal Government and the Recipient when carrying out the activity contemplated by the CA.

c. No fee or profit is allowed

Structure of Award: The CAs will consist of an initial 12-month Base award that will be executed and considered a “seedling” CA. Each seedling CA will provide for the potential for exercising an option, adding up to three more years to the base award, as outlined in the table above. This proposed additional effort may be scoped for one to three years based on its objectives and available funding, will be collaboratively developed with input from Government scientists during the period of performance of the seedling CA, and will be presented in the January 2024 time frame for Cycle 5.

Proposal Submission: The application process consists of proposal submissions from applicants for each

Cycle under this FOA. Applicants should note there are page limitations and other requirements associated with the submission process. Submissions in connection with this FOA are due by the date and time specified below. FOA amendments for future topics will include the submission requirements for those submissions. The Government’s decision to award a seedling CA will be based upon the evaluation results of the proposal submission.

Period of Performance: The CA Awards made as a result of this FOA will provide for a period of performance of one year, with the potential exercise an option period for up to three additional years at the discretion of the Government.

Place of Performance: There is no limitation on the place of performance, except an application must address planned Recipient team member participation during the Innovation Summit Series described above. The locations and other pertinent information for the summit will be posted on STRONG Program website at https://www.arl.army.mil/strong and also available upon request via email to strong-cra@army.mil.

Collaboration: Collaboration is expected as described in Section A.1.

Funding: This FOA is issued subject to the availability of funds. ARL has submitted the requisite documents to request funding for the period covered by the program. However, Applicants are reminded this request is subject to Presidential, Congressional and Departmental approval. Funding levels specified in this FOA are estimated funding levels and are for proposal preparation purposes only; actual funding levels of the CAs will be updated annually as part of the federal appropriation process.

Profit/Fee: Profit/fee is not permitted under the CA.

Cost Sharing: Cost sharing is not required under this FOA.

Opportunity Webinar: ARL will host an opportunity webinar (see Event timeline above). A link to the webinars can be requested via email to strong-cra@army.mil and will be posted on the STRONG Program website at https://www.arl.army.mil/strong.

Contact Information: Additional information about scheduling and events related to the STRONG CRA is available upon request via email to strong-cra@army.mil. All questions or comments concerning this FOA shall be submitted to strong-cra@army.mil. Comments or questions submitted should be concise and to the point. In addition, the relevant part and paragraph of the FOA to which a comment or question pertains must be referenced. Responses to non-proprietary questions received will be answered at the STRONG opportunity webinar and posted in the grants.gov announcement for the benefit of all interested parties. All clearly identified and marked proprietary questions submitted will be responded to via an individual email response, and not responded to at the opportunity webinar. Applicants are encouraged to submit questions as early as possible. The deadline for submission of questions which will be answered under this FOA is listed in Event timeline above. Any answers provided to questions do not change the requirements of this FOA.

Future amendments to this FOA, including new cycle topics, will be issued via an amended FOA posted in grants.gov.

C. ELIGIBILITY INFORMATION

1. Eligible Applicants

It is our goal for the program to include a diverse group of Applicants with varied long-term interests.

Participants may be institutions of higher education, for-profit, or non-profit organization.

Federally Funded Research and Development Centers (FFRDCs) are subject to applicable direct competition limitations and cannot propose to this solicitation in any capacity unless they meet the following conditions. (1) FFRDCs must clearly demonstrate that the proposed work is not otherwise available from the private sector. (2) FFRDCs must provide a letter, on official letterhead from their sponsoring organization, that (a) cites the specific authority establishing their eligibility to propose to Government solicitations and compete with industry, and (b) certifies the FFRDC’s compliance with the associated FFRDC sponsor agreement’s terms and conditions. These conditions are a requirement for FFRDCs proposing to be awardees or subawardees.

. Proposals may consist of teams from any combination of organizations (e.g., prime and subawardees), but this is not a requirement for award and award will only be made to a single entity.

For each Cycle, only those Applicants awarded a seedling CA will be eligible for consideration of a 3-year option period for that Cycle as a prime during the follow on presentations that will occur in the January 2024 timeframe; however, if desired, a Recipient of a seedling CA can include new sub-awardees in their white paper and follow on presentation. All eligible Applicants may submit an application for future topics under future cycles.

2. Cost Sharing or Matching

Cost sharing is not required under this Funding Opportunity.

D. APPLICATION AND SUBMISSION INFORMATION

The application process consists of a single Proposal stage. Applicants will receive feedback regarding their proposal ONLY IF IT IS SELECTED FOR AWARD, in order to improve the proposal and ensure alignment of the proposed research with Government goals. Applicants with non-selected proposals will be notified of their non-selection, but will not receive feedback.

1. Address to Request Application Package

This Funding Opportunity may be accessed from the following: Grants.gov (www.grants.gov).

Amendments, if any, to this FOA will be posted to these websites when they occur. Interested parties are encouraged to periodically check these websites for updates and amendments.

2. Content and Format of Application Submission

The following information is for those wishing to respond to the FOA:

Grants.gov Application Submission and Receipt Procedures

This section provides the application submission and receipt instructions for DoD program applications. Please read the following instructions carefully and completely.

DoD is participating in the Grants.gov initiative to provide the grant community with a single site to find and apply for grant funding opportunities. For this funding opportunity, DoD requires applicants to submit their applications online through Grants.gov. This funding opportunity may be found on Grants.gov by going to the Grants.gov Search Grants screen and entering the funding opportunity number for this FOA, W911NF-19-S-0001, in the Funding Opportunity search box. You can also search for the CFDA Number 12.630

How to Register to Apply through Grants.gov

1. Instructions: Read the instructions below about registering to apply for DoD funds.

Applicants should read the registration instructions carefully and prepare the information requested before beginning the registration process. Reviewing and assembling the required information before beginning the registration process will alleviate last-minute searches for required information.

Organizations must have a active System for Award Management (SAM) registration and Grants.gov account to apply for grants. Creating a Grants.gov account can be completed online in minutes, but SA registrations may take additional time. Therefore, an organization's registration should be done in sufficient time to ensure it does not impact the entity's ability to meet required application submission deadlines.

Complete organization instructions can be found on Grants.gov here:

https://www.grants.gov/web/grants/applicants/organization-registration.html

a. Register with SAM: All organizations applying online through Grants.gov must register with the System for Award Management (SAM). Failure to register with SAM will prevent your organization from applying through Grants.gov. SAM registration must be renewed annually. For more detailed instructions for registering with SAM, refer to:

https://www.grants.gov/web/grants/applicants/organization-registration/step-2- register-with-sam.html

b. Create a Grants.gov Account: The next step is to register an account with Grants.gov. Follow the on-screen instructions or refer to the detailed instructions here:

https://www.grants.gov/web/grants/applicants/registration.html

c. Add a Profile to a Grants.gov Account: A profile in Grants.gov corresponds to a single applicant organization the user represents (i.e., an applicant) or an individual applicant. If you work for or consult with multiple organizations and have a profile for each, you may log in to one Grants.gov account to access all of your grant applications. To add an organizational profile to your Grants.gov account, enter the UEI Number for the organization in the UEI field while adding a profile. For more detailed instructions about creating a profile on Grants.gov, refer to:

https://www.grants.gov/web/grants/applicants/registration/add-profile.html

d. EBiz POC Authorized Profile Roles: After you register with Grants.gov and create an Organization Applicant Profile, the organization applicant's request for Grants.gov roles and access is sent to the EBiz POC. The EBiz POC will then log in to Grants.gov and authorize the appropriate roles, which may include the Authorized Organization Representative (AOR) role, thereby giving you permission to complete and submit applications on behalf of the organization. You will be able to submit your application online any time after you have been assigned the AOR role. For more detailed instructions about creating a profile on Grants.gov, refer to:

https://www.grants.gov/web/grants/applicants/registration/authori ze-roles.html

e. Track Role Status: To track your role request, refer to:

https://www.grants.gov/web/grants/applicants/registration/trac k-role-status.html

2. Electronic Signature: When applications are submitted through Grants.gov, the name of the organization applicant with the AOR role that submitted the application is inserted into the signature line of the application, serving as the electronic signature. The EBiz POC must authorize people who are able to make legally binding commitments on behalf of the organization as a user with the AOR role; this step is often missed, and it is crucial for valid and timely submissions.

How to Submit an Application to DoD via Grants.gov

Grants.gov applicants can apply online using Workspace. Workspace is a shared, online environment where members of a grant team may simultaneously access and edit different webforms within an application. For each funding opportunity announcement (FOA), you can create individual instances of a workspace.

Below is an overview of applying on Grants.gov. For access to complete instructions on how to apply for opportunities, refer to:

https://www.grants.gov/web/grants/applicants/workspace-overview.html

a. Create a Workspace: Creating a workspace allows you to complete it online and route it through your organization for review before submitting.

b. Complete a Workspace: Add participants to the workspace to work on the application together, complete all the required forms online or by downloading PDF versions, and check for errors before submission.