Understanding how humans and AI or robotic agents can work together effectively requires a shared foundation for experimentation. A University of Michigan-led team developed a new taxonomy to serve as a common language among researchers, then used it to evaluate current testbeds used to study how human-agent teams will perform.
A common language to describe and assess human–agent teams
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