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The future vision for the U.S. Army includes teams of humans and intelligent agents working together to accomplish missions. The U.S. Army Combat Capabilities Development Command’s Army Research Laboratory (ARL) has established this new collaborative program, Strengthening Teamwork for Robust Operations in Novel Groups (STRONG), with the 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 technologies. It 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. This effort addresses the teamwork (states and processes) that is critical to the future vision of human-agent teaming in the military. DeCostanza et al. (IEEE, 2018) provide a detailed vision and discussion of some of the scientific questions critical to achieving this goal. Importantly, this interactive paper also provides a public forum for commentary and feedback from the scientific community at large and will serve as a valuable resource for understanding the research objectives of this program.
- Enhancing Human-Agent Teaming
- STRONG Cycle 2 Funding Opportunity
- Cycle 2 Funding Opportunity on grants.gov
- To be determined
STRONG Cycle 1: Information and Updates
STRONG Cycle 1 (FY19) was intended to set the stage with fundamental research aimed at theories of team-level processes for heterogeneous human-agent teams. Nine seedlings addressing foundational science focused on identifying and characterizing the critical states and processes for effective performance in human-agent teams.
At the end of the 2019 Summer Innovation Summit, collaborative proposals from 3 groups were requested for 3-year extensions to further explore:
- Prediction and computational learning of human attributes, dynamics and roles for individualized agent adaptation in human-agent teams (Prime: Carnegie Mellon University)
- Novel micro- (e.g., physiology) and meso- (e.g., social behavior) scale signatures during human-agent interaction (Prime: Northeastern University)
- Macro-scale emergence of human-agent team processes (Prime: Northwestern University)
Two complementary, collaborative CCDC ARL projects were also initiated exploring coordinated physiological representations in human-agent teams and adaptive social decision making. This ongoing Cycle 1 research is focused on identifying and characterizing the critical states and processes for effective performance in human-agent teams.
Summaries from all nine seedlings and the five follow-on efforts (internal and external) can be found at the below links:
- STRONG Cycle 1 (FY19) Funded Proposal Summaries
- Adaptation of Social Decision Making under Uncertainty in Human-Agent Teams
- On a generalized framework of physiological synchrony underlying coordinated physiological representations in human-autonomy teams
- Macro Signatures of Success in Human-Autonomy Teams
- Micro and Meso Signatures of Success in Human-Autonomy Teams
- Individualized Adaptation in Human Agent Teams
Important Program Information
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) and shares a common vision of highly collaborative academia-industry-government partnerships; however, it will be executed with a program model different than previous ARL Collaborative Research/Technology Alliances. Specific components of the program are highlighted below:
- STRONG will be executed through a series of eight annual program cycles (i.e., Cycles 1-8). The FOA will be amended annually to identify a specific problem statement, or topic, for that specific Cycle. The topic for each Cycle will be chosen to systematically converge on the specific long-term program goal.
- Eight new topics (Cycles 1-8) are expected from FY19-FY26, with each topic focused on addressing a different scientific area within the scope of the broad research aims of STRONG. These topics will be carefully chosen based on both program achievements from the previous year and on scientific and technological advancements by the broader research community.
- For each topic, funding will be provided to those Recipients selected for 1 year under a cooperative agreement (CA) described as the “seedling” project.
- The Recipients of a “seedling” CA are then eligible to receive funding for a single optional extension of up to 3 years at the conclusion of the “seedling” project. The period of the performance of the option will be based on the research and available funding. It is envisioned that “seedling” Recipients will work with government researchers and/or other “seedling” Recipients to collaboratively develop a proposal for an optional extension to the initial seedling CAs. Opportunities for planning and enabling these collaborations in support of an option will be provided at the annual Summer Innovation Summits (see below), as well as via regular communication between “seedling” Recipients and government researchers.
- Recipient participation by at least one team member in each week of the Innovation Summit Series under the Recipient’s awarded Cycle will be REQUIRED.
- 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.
For full program opportunity details, click the Program Announcement link on this page.