  We study the mixed human-robot team design in a system theoretic setting
using the context of a surveillance mission. The three key coupled components
of a mixed team design are (i) policies for the human operator, (ii) policies
to account for erroneous human decisions, and (iii) policies to control the
automaton. In this paper, we survey elements of human decision-making,
including evidence aggregation, situational awareness, fatigue, and memory
effects. We bring together the models for these elements in human
decision-making to develop a single coherent model for human decision-making in
a two-alternative choice task. We utilize the developed model to design
efficient attention allocation policies for the human operator. We propose an
anomaly detection algorithm that utilizes potentially erroneous decision by the
operator to ascertain an anomalous region among the set of regions surveilled.
Finally, we propose a stochastic vehicle routing policy that surveils an
anomalous region with high probability. Our mixed team design relies on the
certainty-equivalent receding-horizon control framework.
