Designing Agile Human-Machine Teams

DARPA seeks mathematical methods to enable a general-purpose design tool for optimizing human-machine teams in a diverse range of national security contexts

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. As artificial intelligence becomes more advanced, the future of kinetic, cyber, and electronic warfare envisions humans and intelligent machines working together as a team. 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 today 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," said John Paschkewitz, DARPA program manager. "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-Teams results could also apply to complexity and tempo challenges to team performance in non-combat applications, such as scientific and drug discovery, software engineering, logistics planning, advanced hardware engineering, and intelligence forecasting. 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 results of A-Teams could also be applied to enhance human-machine collaboration technologies being developed in various DARPA programs 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.

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). A special notice providing details about a Proposers Day to be webcast on Dec. 7, 2016, is available on FedBizOppps here:

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