  In a search and rescue scenario, rescuers may have different knowledge of the
environment and strategies for exploration. Understanding what is inside a
rescuer's mind will enable an observer agent to proactively assist them with
critical information that can help them perform their task efficiently. To this
end, we propose to build models of the rescuers based on their trajectory
observations to predict their strategies. In our efforts to model the rescuer's
mind, we begin with a simple simulated search and rescue task in Minecraft with
human participants. We formulate neural sequence models to predict the triage
strategy and the next location of the rescuer. As the neural networks are
data-driven, we design a diverse set of artificial "faux human" agents for
training, to test them with limited human rescuer trajectory data. To evaluate
the agents, we compare it to an evidence accumulation method that explicitly
incorporates all available background knowledge and provides an intended upper
bound for the expected performance. Further, we perform experiments where the
observer/predictor is human. We show results in terms of prediction accuracy of
our computational approaches as compared with that of human observers.
