Interaction flexibility in artificial agents teaming with humans

Team interaction involves the division of labor and coordination of actions between members to achieve a shared goal. Although the dynamics of interactions that afford effective coordination and performance have been a focus in the cognitive science community, less is known about how to generate these flexible and adaptable coordination patterns. This is important when the goal is to design artificial agents that can augment and enhance team coordination as synthetic teammates. Although previous research has demonstrated the negative impact of model-based agents on the pattern of interactions between members using recurrence quantification methods, more recent work utilizing deep reinforcement learning has demonstrated a promising approach to bootstrap the design of agents to team with humans effectively. This paper explores the impact of artificial agent design on the interaction patterns that are exhibited in human-autonomous agent teams and discusses future directions that can facilitate the design of human-compatible artificial agents.