STRONG FOA Cycle 4 - FINAL.pdf

UNCLASSIFIED/Approved for Public Release/Distribution Unlimited

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

W911NF-19-S-0001

Amendment #6

The purpose of this modification is to issue Cycle 4 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 by 3:00pm (local time in North Carolina, USA) on 16 May 2022. 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 4 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 4 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 April 2022 Opportunity Webinar 20 April 2022 Deadline for Questions on Funding Opportunity 5 May 2022 Proposals due for Cycle 4 16 May 2022 Cycle 4 Awards June 2022 (Expected) Cycle 4 Innovation Summit July/August 2022 Cycle 4 Follow On Presentations January 2023

A. PROGRAM DESCRIPTION

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) in 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.

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.

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 2023.

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

4. Cycle 4 Updates

a) Topic: Abstracting the brain’s sub-architectures to enable rapid adaptation and reconfiguration in future human machine teams

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. Cycle 4 of STRONG focuses on the question of how the functionality of algorithms underlying intelligent systems influences the performance of mixed human-intelligent agent teams within complex, dynamic tasks that require adaptation. Specifically, Cycle 4 asks 1) if machine learning embedding high resolution mammalian sub-architecture neuro-mimicry is associated with more effective human-intelligent agent interactions than other forms of machine learning; 2) if embedding high-resolution mammalian sub-architectures other than visual processing (e.g., spatial reasoning neuro-mimicry) into human-guided machine learning can outperform deep learning for non-visual processing tasks (e.g., spatial reasoning tasks) that require adaptation; and 3) how mammalian brain sub-architecture neuro-mimicry based machine learning can be integrated with humans to enhance performance on complex, dynamic tasks that require team adaptation. Cycle 4 “Seedlings” are expected to demonstrate the potential to answer one or more of these three questions. Cycle 4 “Multi-Year Options” are expected to address all three questions within the multidisciplinary, collaborative “building block” approach of the STRONG program.

Background:

Neuro-mimicry: Human behavior is flexible and adaptive across perspectives and contexts in ways that current machine intelligence is not (Metcalfe, et al 2021). In order to create this flexible and adaptive behavior the human brain contains thousands of sub-architectures each representing the world in divergent and yet intertwined ways (Hawkins, 2019). Traditional deep learning networks that form the foundation for many current human machine teaming paradigms are based on a high-resolution mimicry of one of these sub-architectures, i.e. the mammalian visual system (Hassabis 2017). Given this, it is perhaps unsurprising that while deep learning networks have had great success in some areas such as image recognition, they have fallen short in non-visual processing tasks,e.g., under conditions where they are required to flexibly adapt to novelty or when faced with open ended problem spaces with poorly defined criterion of success (Hassabis 2017). It remains an open question if high-resolution neuro-mimicry of other mammalian sub-architectures can improve performance on the tasks those networks enable within the brain. One example of another sub-architecture that has the potential for such neuro-mimicry is is the spatial reasoning system. The mammalian brain’s ability to simultaneously construct multiple spatial representations of the world (from body and motor centric representations to higher level representations like grid and place cells) is a critical factor underlying human flexibility and adaptability (Nau, at al, 2018). Because humans have constant online access to a wide variety of spatial representations, their brains are able to quickly adapt and reconfigure to changing conditions by choosing the appropriate intertwined set of mappings.

Early evidence suggests mimicking the spatial reasoning system may be beneficial for machine intelligence, i.e., emerging evidence from embedding mathematical spaces similar to those used in the mammalian spatial reasoning system into machine intelligence points to improved performance (e.g., reduced machine training time, improve generality; Love, 2020). Further, there have been significant recent advances in our understanding of how the brain creates these computational spaces, which computational spaces the brain uses, and how the brains multiple spatial reasoning sub-architectures interact, all of which can potentially be capitalized on to improve the performance of human machine teams under novel and changing conditions (Chadhuri et. Al. 2019). Taken together, these advancements are providing a high-resolution understanding of mammalian spatial reasoning sub-architectures and creating the potential to pursue unique machine learning approaches.

Human-Intelligent Agent Teaming: The algorithms underlying the capabilities of machine intelligence directly impact not only their own performance but also the effectiveness of their integration with human teammates. Over the past two decades, a plethora of research has focused on human-system integration approaches () to improve issues underlying team performance (e.g., human-agent trust, system usability). For instance, in order to make deep learning networks less of a black box, networks can be trained, organized, and designed with the specific purpose of making their function more transparent to the human user (Ras et. Al. 2022). However, these approaches often are associated with a commiserate reduction in overall efficacy of the deep learning network. Here we posit that deep networks with embedded high-resolution features from brain sub-architectures, such as the spatial reasoning system, may be more implicitly understandable because their underlying computations are more ‘human’ like, eliminating the need to build in specific design features to explicitly make their inner workings explicable. It remains an open question whether similarities between human and machine algorithms will also extend beyond technology performance to have significant influences on human-machine integration..

STRONG Collaborations and Adaptive Performance: The STRONG efforts envision creating future teams of humans and intelligent agents that are able to tackle complex, open ended, and context dependent problem spaces that are inaccessible to either humans or machine intelligence alone (see Decostanza et al. 2018, Metcalfe et. al. 2021). Critical to the success of these future teams will be their ability to adapt to changing conditions and context in real time. The STRONG program focuses on an approach wherein machine intelligence will need to adapt and work in concert with humans who have significant task domain knowledge but minimal to no specialized training in understanding and training machine intelligence. One technique that has shown great promise in allowing for humans with minimal to no specialized training to adapt the machine intelligence they are teaming with is human guided machine learning (e.g., see Warnell et. al. 2018; Waytowich et al 2018). Techniques like human guided machine learning allows for humans to interact with machine intelligence in an active way, which allow for new roles for human and machine intelligence to be explored.

Cycle 4 Goals and Questions:

In support of the collaborative “building block” approach of the STRONG program, the Cycle 4 call focuses on abstracting the brain’s sub-architectures within the context of human-guidable machine learning. This contextualization is specifically aimed to enable the science to integrate with that of previous and future STRONG calls. Cycle 4 “Seedlings” are expected to demonstrate the potential to answer one or more of the following three primary questions (bolded), while Cycle 4 “Multi-Year Options” are expected to address all three primary questions (bolded). Sub-questions are included to help inspire thought but do not need to be addressed specifically.

Is machine learning with an embedded high resolution mammalian sub-architecture neuro-mimicry associated with more effective human-intelligent agent interactions than other forms of machine learning?

o For example, as discussed above, deep learning embeds a high resolution mammalian neuro-mimicry of the visual processing sub-architecture. Can human-deep learning-based agent interactions outperform other forms of machine learning across a range of strictly visual processing tasks? Are human-deep learning-based agent interactions more resilient to individual human differences that other forms of machine learning across a range of strictly visual processing tasks? Do human-deep learning-based agent interactions improve human’s understanding, predictability of, and/or trust in agent behavior more than other forms of machine learning that are performing at the same levels or above?

Does embedding high-resolution mammalian sub-architectures other than visual processing (e.g., spatial reasoning neuro-mimicry) into human-guided machine learning outperform deep learning for non-visual processing tasks (e.g., spatial reasoning tasks) that require adaptation?

o For example, as discussed above, the mammalian brain’s spatial reasoning system creates a cohesive world view out of hundreds of related sub maps that are complimentary but not redundant (Hawkins 2019). Can a high-resolution spatial reasoning sub-architecture be embedded in a machine learning instantiation that is compatible with human-guided learning?

Does this spatial-reasoning sub-architecture-based human-guided machine learning outperform deep learning-based human-guided machine learning across a range of strictly spatial reasoning tasks that require adaptation (and vice versa on strictly visual processing tasks that require adaptation)? Here “outperform” is used broadly to include decreased times to complete tasks, increased accuracy; avoidance of catastrophic errors; decreased resource requirements to complete task, etc …

Can mammalian brain sub-architecture neuro-mimicry-based machine learning can be integrated within teams of humans and agents to enhance overall performance on complex, dynamic tasks that require team adaptation?

o For example, can human-guided deep learning-based agents and human-guided spatial-reasoning neuro-mimicry-based agents be combined with humans with significant task knowledge, to outperform other forms of human-agent teams on tasks that require all three of visual processing, spatial reasoning, and adaptation? Does the integration of human-guided deep learning-based agents and/or human-guided spatial-reasoning neuro-mimicry-based agents improve performance on tasks that require the dynamic or “on-the-fly” reconfiguration of the roles of humans and agents teams? Does the integration of human-guided deep learning-based agents and/or human-guided spatial-reasoning neuro-mimicry-based agents improve performance on tasks where human team members are replaced over trials?

b) 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).

Notional Activity Outline occurring in the July-August 2022 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 2023 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

Chadhuri, R., Gercek, B.,Pandey, B., Peyrache, A., Fiete, I. ‘The intrinsic attractor manifold and population dyamics of a canonical cognitive circuit across waking and sleep’ Nature Neuroscience 22, 1512-1520 (2019)

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

Hassabis, D., Kumaran, D., Summerfield, C., Botvinick, M., ‘Neuroscience-inspired artificial intellignece’, Neuron 96, 245-259 (2017)

Hawkins, J. ‘A Thousand Brains: A New Theory of Intelligence’ (2021) Basic Books

Love, E., Fillippenko, B., Maroulas, V., Carlsson G. ‘Topological Deep Learning’ in Topological

Convolutional Neural Networks. Eds.: Allen G., Mukerjee, S., Nadler, B., arXiv:2101.05778v2

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

Nau, M., Julian, J., Doeller, C. ‘How the Brain’s Navigation System Shapes our Visual Experience’ Trends in Cognitive Science Vol. 22, No. (2018)

Ras, G., Xie, N., van Gerven, M., Doran, D., Explainable Deep Learning: A Field Guide for the Uninitiated’ Journal of Artificial Intelligence Research 73, 329-396 (2022)

Warnell, G., Waytowich, N., Lawhern, V., Stone, P. ‘Deep tamer: Interactive agent shaping in high dimensional state spaces’ Proceedings of the AAAI 32,(2018)

Waytowich, W., Goecks, V., Lawhern, V. ‘Cycle-of-learning for autonomous systems from human interaction’ Proceedings of the AAAI 32 (2018)

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.

CAs for Institutions of Higher Education and nonprofit organizations are primarily governed by the following:

Federal statutes Federal regulations 2 CFR Part 200, as modified and supplemented by DoD's interim implementation found at 2 CFR Part 1103

CAs for For-Profit Recipients are primarily governed by the following:

Federal statutes Federal regulations 32 CFR Part 34

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 4 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 2023 time frame for Cycle 4.

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.

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 on (see Event timeline above). A link to the webinar will be posted on the STRONG Program website at https://www.arl.army.mil/strong.

Contact Information. Outside of questions posed at the Opportunity Webinar, all questions or comments concerning this FOA shall be submitted to the Government through the STRONG Program website at https://www.arl.army.mil/strong. 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 posted to the STRONG Program website under the “General Information/Questions & Answers" section for the benefit of all interested parties. All clearly identified and marked proprietary questions submitted will be responded to via an individual email response, not posted to the STRONG Program website. 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 (FFRDC) may propose as well, with effort as allowed by their sponsoring agency and in accordance with their sponsoring agency policy. 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 2023 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. The Workspace progress bar will display the state of your application process as you apply. As you apply using Workspace, you may click the blue question mark icon near the upper-right corner of each page to access context-sensitive help.

1) Adobe Reader: If you decide not to apply by filling out webforms you can download individual PDF forms in Workspace. The individual PDF forms can be downloaded and saved to your local device storage, network drive(s), or external drives, then accessed through Adobe Reader.

NOTE: Visit the Adobe Software Compatibility page on Grants.gov to download the appropriate version of the software at:

https://www.grants.gov/web/grants/applicants/adobe-software-compatibility.html

2) Mandatory Fields in Forms: In the forms, you will note fields marked with an asterisk and a different background color. These fields are mandatory fields that must be completed to successfully submit your application.

3) Complete SF-424 Fields First: The forms are designed to fill in common required fields across other forms, such as the applicant name, address, and UEI Number.

Once it is completed, the information will transfer to the other forms.

c. Submit a Workspace: An application may be submitted through workspace by clicking the

Sign and Submit button on the Manage Workspace page, under the Forms tab. Grants.gov recommends submitting your application package at least 24-48 hours prior to the close date to provide you with time to correct any potential technical issues that may disrupt the application submission.

d. Track a Workspace Submission: After successfully submitting a workspace application, a

Grants.gov Tracking Number (GRANTXXXXXXXX) is automatically assigned to the application. The number will be listed on the Confirmation page that is generated after submission. Using the tracking number, access the Track My Application page under the Applicants tab or the Details tab in the submitted workspace.

For additional training resources, including video tutorials, refer to:

https://www.grants.gov/web/grants/applicants/applicant-training.html

Applicant Support: Grants.gov provides applicants 24/7 support via the toll-free number 1-800- 518-4726 and email at support@grants.gov. For questions related to the specific grant opportunity, contact the number listed in the application package of the grant you are applying for.

If you are experiencing difficulties with your submission, it is best to call the Grants.gov Support Center and get a ticket number. The Support Center ticket number will assist the DoD with tracking your issue and understanding background information on the issue.

Application forms and instructions will be available at Grants.gov. To access these materials, go to http://www.grants.gov, select "Apply for Grants", and then select "Download an Application Package." Enter the FOA number, W911NF-19-S-0001.

Applicants must complete the mandatory forms and any optional forms (e.g., SF-LLL Disclosure of Lobbying Activities) in accordance with the instructions on the forms and the additional instructions below. The required fields should be completed in accordance with the “pop-up” instructions on the forms. To activate the instructions, turn on the “Help Mode” (icon with the pointer and question mark at the top of the form). Files that are attached to the forms must be in Adobe Portable Document Form (PDF) unless otherwise specified in this announcement.

The following formatting rules apply for the file attachments:

Paper size when printed – 8.5 x 11 inch paper

Margins – 1 inch Spacing – Single Font – No Smaller than Times New Roman, 12 point

Form: SF 424 (R&R) (Mandatory) – Complete this form first to populate data in other forms.

Authorized Organization Representative (AOR) usernames and passwords serve as “electronic signatures” when your organization submits applications through Grants.gov. By using the SF 424 (R&R), applicants are providing the certification required by 32 CFR Part 28 regarding lobbying.

Form: Research & Related Other Project Information - Complete questions 1 through 6 and attach files.

Project Summary/Abstract (Field 7 on the form) - The Project Summary should be a brief summary of the content of the application. It shall include a title, the research team (include roles, expertise, affiliations), designation of Junior Investigator or Senior Investigator derivation, and a brief abstract articulating the project objectives. The project summary/abstract must not exceed 1 page and will not be evaluated as it is primarily for documentation purposes.

Project Narrative (Field 8 on the form) - Chapters and Numbers of pages – Field 7 is to contain the chapters set forth below and may not exceed the stipulated page counts for those chapters.

Pages in excess of the page limits may be removed for the evaluation of the application. All chapters set forth below should be in a single PDF file. For those chapters with specified page limitations, any pages submitted beyond the specified amount for a chapter will not be reviewed or evaluation.

Chapter 1: Technical Component. The pages included in Chapter 1 are to be numbered.

Applicants are advised that Chapter 1 will not exceed 8 pages, utilizing one side of the page. Tables that extend beyond one page (fold out tables) will only count as one page.

Pages provided in excess of the prescribed 8 pages will not be evaluated.

Proposed Effort (approximately 4 pages): This section of Chapter 1 should include an overview of the research strategy to be employed to advance the state-of-the-art in advanced preparation for a future teaming environment with fluid, mutual adaptation between humans and agents that leads to enhanced team performance over time and tasks; a short description and justification for annual research goals of the proposed effort; and a short technical discussion stating the background and objectives of the proposed research, the overall technical approaches to be pursued. This technical discussion should include a proposed breakdown of research tasks and short description of the technical approaches for each task. The Proposed Effort should include the specific research questions and/or hypotheses to be tested, and what specific tasks will be performed by the research team to test them, as well as justification for why these are the appropriate measures. Technical Proposals must also include a brief summary of how this research addresses the broader needs of the program, as summarized in the program description above.

Proposed Innovation Summit Series Participation and Collaboration

Development (approximately 1 pages): Must include the plan for participation in the Innovation Summit Series. Collaboration with US Army Research Lab or other Government research personnel is highly encouraged, as well as cross-connection with other Program participants. Include a plan to foster communication and collaboration across government, academic, and industry partners, through planned participation in the Innovation Summit Series and other methods for developing multi-disciplinary collaborations throughout performance under the seedling CA. Proposals should also focus on the plan for development of collaborations to propose a follow-on option effort.

Participant(s) roles, qualifications and bio-sketches (approximately 1 pages): Must include the names, primary role/availability, and brief biographies of individuals involved with the research. Include plans for junior investigator development and mentorship of less experienced personnel (mentoring plan).

Proposed timeline (approximately 0.5 page): An estimated timeline of tasks to be completed during the 12-month period, including (1) components to be completed at the home institution(s) and (2) components planned during the Summit series.

Chapter 2: Cost Component. The pages included in Chapter 2 will be numbered and

Chapter 2 does not have a page limitation. Cost Application must include a budget for the 12 month period of performance. The cost portion of the application will contain cost estimates sufficiently detailed for meaningful evaluation, to include Summit series participation. Budget justification may also be attached in this chapter.