Human AI Team
Human-AI teaming research explores how humans and artificial intelligence agents can collaborate effectively to achieve shared goals, focusing on optimizing team performance and understanding the dynamics of human-AI interaction. Current research emphasizes developing AI agents with capabilities like theory of mind and explanation generation, employing reinforcement learning and other machine learning techniques to manage task delegation and optimize team strategies, and evaluating the impact of different AI roles (e.g., recommender, analyzer) on team effectiveness. This field is significant for advancing both our understanding of human-computer interaction and for developing practical applications across diverse domains, including decision-making, command and control, and collaborative problem-solving.
Papers
Improving the State of the Art for Training Human-AI Teams: Technical Report #3 -- Analysis of Testbed Alternatives
Lillian Asiala, James E. McCarthy, Lixiao Huang
Improving the State of the Art for Training Human-AI Teams: Technical Report #2 -- Results of Researcher Knowledge Elicitation Survey
James E. McCarthy, Lillian Asiala, LeeAnn Maryeski, Dawn Sillars
Improving the State of the Art for Training Human-AI Teams: Technical Report #1 -- Results of Subject-Matter Expert Knowledge Elicitation Survey
James E. McCarthy, Lillian Asiala, LeeAnn Maryeski, Nyla Warren