Literal or Pedagogic Human? Analyzing Human Model Misspecification in
  Objective Learning
  It is incredibly easy for a system designer to misspecify the objective for
an autonomous system ("robot''), thus motivating the desire to have the robot
learn the objective from human behavior instead. Recent work has suggested that
people have an interest in the robot performing well, and will thus behave
pedagogically, choosing actions that are informative to the robot. In turn,
robots benefit from interpreting the behavior by accounting for this pedagogy.
In this work, we focus on misspecification: we argue that robots might not know
whether people are being pedagogic or literal and that it is important to ask
which assumption is safer to make. We cast objective learning into the more
general form of a common-payoff game between the robot and human, and prove
that in any such game literal interpretation is more robust to
misspecification. Experiments with human data support our theoretical results
and point to the sensitivity of the pedagogic assumption.
