Modeling Conceptual Understanding in Image Reference Games
  An agent who interacts with a wide population of other agents needs to be
aware that there may be variations in their understanding of the world.
Furthermore, the machinery which they use to perceive may be inherently
different, as is the case between humans and machines. In this work, we present
both an image reference game between a speaker and a population of listeners
where reasoning about the concepts other agents can comprehend is necessary and
a model formulation with this capability. We focus on reasoning about the
conceptual understanding of others, as well as adapting to novel gameplay
partners and dealing with differences in perceptual machinery. Our experiments
on three benchmark image/attribute datasets suggest that our learner indeed
encodes information directly pertaining to the understanding of other agents,
and that leveraging this information is crucial for maximizing gameplay
performance.
