Adapt and overcome: Perceptions of adaptive autonomous agents for human-AI teaming

Rapid advances in AI technologies have caused teams to explore the use of AI agents as full, active members of the team. The complex environments that teams occupy require human team members to constantly adapt their behaviors, and thus the ability of AI teammates to similarly adapt to changing situations significantly enhances the team's chances to succeed. In order to design such agents, it is important that we understand not only how to identify the amount of autonomous control AI agents have over their decisions, but also how changes to this control cognitively affects the rest of the team. Professional organizations often break their work cycles into phases that set limits on the team members' actions, and we propose that a similar process could be used to define the autonomy levels of AI teammates. Cyber incident response is an ideal context for this proposal, as we were able to use incident response phases to explore how a team's work cycle could guide an AI agent's changing level of autonomy. Using a mixed methods approach, we recruited 103 participants to complete a factorial survey containing ten contextual vignettes focused on an AI teammate's level of autonomy in incident response contexts, and from these participants we conducted twenty-two follow-on qualitative interviews that further explored how the participants felt an AI agent's adaptive capabilities would affect team performance and cohesiveness. Our results showed that work cycles can be used to assign autonomy levels to adaptive AI agents based upon the degree of formal processes and predictability of the team's tasks during the cycle, and that dynamic, human-like adaptation methods are vital to effective human-AI teams. This research provides significant contributions to the HCI community by proposing design recommendations for the development of adaptive autonomous teammates that both enhance Human-AI teams' productivity and promote positive team dynamics.