LAUSR

Teaming permits cognitively complex work to be rapidly executed by multiple entities. As artificial agents (AAs) participate in increasingly complex cognitive work, they hold the promise of moving beyond tools to becoming effective members of human–agent teams. Coordination has been identified as the critical process that enables effective teams and is required to achieve the vision of tightly coupled teams of humans and AAs. This paper characterizes coordination on the axes of types, content, and cost. This characterization is grounded in the human and AA literature and is evaluated to extract design implications for human–agent teams. These design implications are the mechanisms, moderators, and models employed within human–agent teams, which illuminate potential AA design improvements to support coordination.