Predicting task performance for intelligent human-machine interactions - Heard, Jamison; Baskaran, Prakash; Adams, Julie A. (Frontiers in Neurorobotics, 2022)
Human-machine teams are deployed in a diverse range of task environments and paradigms3 that may have high failure costs (e.g., nuclear power plants). It is critical that the machine4 team member can interact with the human effectively without reducing task performance. These5 interactions may be used to manage the human’s workload state intelligently, as overall workload6 is related to task performance. Intelligent human-machine teaming systems rely on a facet of the7 human’s state to determine how an interaction occurs, but typically only consider the human’s8 state at the current time step. Future task performance predictions may be leveraged to determine9 if adaptations need to occur in order to prevent future performance degradation. An individualized10 task performance prediction algorithm that relies on a multi-faceted human workload estimate is11 shown to predict a supervisor’s task performance accurately. The analysis varies the prediction12 time frame (0 seconds to 300 seconds) and compares results to a generalized algorithm.