Model Elicitation through Direct Questioning
  The future will be replete with scenarios where humans are robots will be
working together in complex environments. Teammates interact, and the robot's
interaction has to be about getting useful information about the human's
(teammate's) model. There are many challenges before a robot can interact, such
as incorporating the structural differences in the human's model, ensuring
simpler responses, etc. In this paper, we investigate how a robot can interact
to localize the human model from a set of models. We show how to generate
questions to refine the robot's understanding of the teammate's model. We
evaluate the method in various planning domains. The evaluation shows that
these questions can be generated offline, and can help refine the model through
simple answers.
