Fatigue and Task Load Dependent Decision Referrals for Joint Binary Classification in Human-Automation Teams

We consider a human-automation team jointly solving binary classification tasks over multiple time stages. At each stage, the automation observes the data for a batch of classification tasks, classifies a subset of them and refers the others to the human. The human’s performance depends on task load and fatigue, where fatigue is modeled as a controlled Markov process dependent on the past task loads. We formulate the automation’s problem of deciding which tasks to refer as a Markov decision process and present a sampling-based approximate dynamic program that leverages task independence across time and the structure of the recently obtained single-stage optimal allocation policy. We then present a numerical study comparing our solution against a baseline policy that does not explicitly account for fatigue dynamics.