Modern military operations are dynamic and complex—requiring, for example, that infantry squads carry out their missions simultaneously in the 3-dimensional physical world, the cyber domain, and across the electromagnetic spectrum. Similarly, complexity and tempo are challenges to team operations in other applications including scientific and drug discovery, software engineering, logistics planning, advanced hardware engineering, and intelligence forecasting. Problem solving in these complex environments exceeds the capability of any individual operator and is addressed using teams
Simultaneously, autonomy and artificial intelligence are increasingly utilized to augment and automate operations executed by teams in these environments such as command and control/battle management or cyber/electronic warfare operations. A challenge in designing human-machine systems, however, is determining how best to meld human cognitive strengths and the unique capabilities of smart machines to create intelligent teams adaptive to rapidly changing circumstances.
To address this challenge, DARPA announced the Agile Teams (A-Teams) program, which sets out to discover, test, and demonstrate predictive and generalizable mathematical methods to enable optimized design of agile hybrid teams. A-Teams seeks to fundamentally challenge the current paradigm of human-intelligent machine systems design by changing the focus from simply using machines for automation and substitution of human capacity to an integrated fabric enabling superior collective problem solving.
“A-Teams is focused not on developing new AI technologies per se, but on developing a framework for optimizing the use of smart machines in various roles together with humans to ensure optimal human-machine teamwork for solving dynamic problems,” DARPA program manager John Paschkewitz explained in a press release. “Given an uncertain environment and fluid team structure, how does one best use combined human and machine capabilities to make wise decisions? Are there generalizable mathematical abstractions to capture the dynamic interactions of problem space, team structure, and performance? These are the kinds of questions we intend to answer in the program.” A-team outputs will include algorithms, abstractions and architectures for a machine-based “intelligent fabric” aiming to improve decision-making on the field.
This DAPA program is also in sync with US third offset strategy, announced in November 2014, by then–Secretary of Defense Chuck Hagel. This prime aim is to overcome anti-access and anti-area denial. Deputy Secretary of Defense Bob Work said, “We don’t face a single monolithic or implacable adversary like we did in the Cold War. We face multiple potential competitors, from small regional states like North Korea and Iran, to large advanced states like Russia and China, to non-state adversaries and actors with advanced capabilities. Each of these are probably going to require a different approach and a different strategy, which is why we actually say “offset strategies.”
One of the key points, Deputy Secretary of Defense Bob Work gave greater insight into was Human-machine collaboration. Human-machine collaboration, specifically the ways machines can help humans with decision-making. This teams up human insight with the tactical acuity of computers by allowing machines to help humans make better, faster decisions. Pairing the two will combine the ability of humans to think on the fly with the quick problem-solving methods of artificial intelligence. Work pointed to the advanced helmet on the F-35 joint strike fighter, which fuses data from multiple systems into one layout for the pilot.
In response to third offset strategy DARPA has launched several programs to enhance human-machine collaboration technologies apart from A-Teams such as Resilient Synchronized Planning and Assessment for the Contested Environment (RSPACE), Collaborative Operations in Denied Environment (CODE), Distributed Battle Management (DBM), System of Systems Integration Technology and Experimentation (SoSITE), and others.
Agile teams are defined as teams that are capable of responding to changes in goals, operating environment or team interactions such that the performance of the team is unaffected. This agility may be realized by having high robustness, flexibility, and/or resilience. Broad capability is difficult to realize in very small teams while agility is difficult to realize in large organizations; for this reason, teams of more than five and less than fifty people are of primary interest in this program.
Hybrid teams are teams that utilize intelligent machine elements to augment the performance of the team. Hybrid teams that utilize intelligent machine elements to enhance agility are the primary focus of this program. Intelligent machine elements can have a variety of possible instantiations: for example, as machine agents that are capable of peer-level interaction with, and possible substitution of, the human team members for execution of the goals of the team; or an intelligent problem solving substrate that manages team member communications and capabilities at both the individual and joint level. These intelligent machine elements may utilize a variety of context-appropriate artificial intelligence approaches and not just machine learning.
Designing Agile Human-Machine Teams
The goal of the Agile Teams (A-Teams) program is to discover, test, and demonstrate predictive and generalizable mathematical abstractions for the design of agile hybrid teams. Specifically, A-Teams will develop predictive and practically implemented abstractions and algorithms encoding the dynamic co-evolution of team structure and team problem solving strategies.
Problem solving in these complex environments exceeds the capacity of any individual and is best addressed by teams of people augmented by technology, such as with computer-aided design and collaborative work tools. A-Teams seeks to facilitate a leap forward in teamwork in which more intelligent machines in the future could not only provide automated insights but also serve as decision and interaction facilitators among team members.
The program focus will be on mathematical methods for designing optimal hybrid teams of humans and intelligent machine elements that will be demonstrated and validated in dynamic and complex problem-solving contexts using experimental testbeds. Intelligent machine elements could take a variety of possible forms, including machine agents capable of peer-level interaction with human team members for executing team goals, or as an intelligent problem solving workspace that can coordinate communications and task assignment to optimize team performance.
Intended A-Teams outputs include abstractions, algorithms, and architectures for a machine-based “intelligent fabric” that would dynamically mitigate gaps in ability, improve team decision making, and accelerate realization of collective goals. The program seeks expertise in mathematics, organizational theory, operations research, planning and scheduling, cognitive science, human factors, autonomy, and citizen science (for experimental testbeds).
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