  As environments involving both robots and humans become increasingly common,
so does the need to account for people during planning. To plan effectively,
robots must be able to respond to and sometimes influence what humans do. This
requires a human model which predicts future human actions. A simple model may
assume the human will continue what they did previously; a more complex one
might predict that the human will act optimally, disregarding the robot;
whereas an even more complex one might capture the robot's ability to influence
the human. These models make different trade-offs between computational time
and performance of the resulting robot plan. Using only one model of the human
either wastes computational resources or is unable to handle critical
situations. In this work, we give the robot access to a suite of human models
and enable it to assess the performance-computation trade-off online. By
estimating how an alternate model could improve human prediction and how that
may translate to performance gain, the robot can dynamically switch human
models whenever the additional computation is justified. Our experiments in a
driving simulator showcase how the robot can achieve performance comparable to
always using the best human model, but with greatly reduced computation.
