Examining human-autonomy team interaction and explicable behavior in a dynamic LEGO construction task

The primary goal of this experiment is to examine and build dynamical systems models which specify the optimum coordination area that enables teams to perform better when they collaborate with an autonomous agent in the context of explicable behavior. In this preliminary study, we examine team coordination dynamics and explicable behavior by using Joint Recurrence Plot (RP) and Joint Recurrence Quantification Analysis (JRQA). In our example, visualizations of the interaction patterns show when explicable behavior happened, notably, during unexpected events, e.g., when there was a missing LEGO brick. Our preliminary data provides some initial findings about team interaction under dynamical changes along with content under uncertainty. Current and future work is focused on additional experimentation with three types of team configurations: allhuman, human-agent, and human-multiagent. Through more experimentation, additional insights and examples of other unexpected events will be able to highlight any necessary additional requirements needed for effective teamwork.