  In this work we introduce a structured signaling game, an extension of the
classical signaling game with a similarity structure between meanings in the
context, along with a variant of the Rational Speech Act (RSA) framework which
we call structured-RSA (sRSA) for pragmatic reasoning in structured domains. We
explore the behavior of the sRSA in the domain of color and show that pragmatic
agents using sRSA on top of semantic representations, derived from the World
Color Survey, attain efficiency very close to the information theoretic limit
after only 1 or 2 levels of recursion. We also explore the interaction between
pragmatic reasoning and learning in multi-agent reinforcement learning
framework. Our results illustrate that artificial agents using sRSA develop
communication closer to the information theoretic frontier compared to agents
using RSA and just reinforcement learning. We also find that the ambiguity of
the semantic representation increases as the pragmatic agents are allowed to
perform deeper reasoning about each other during learning.
