  Research in multi-agent cooperation has shown that artificial agents are able
to learn to play a simple referential game while developing a shared lexicon.
This lexicon is not easy to analyze, as it does not show many properties of a
natural language. In a simple referential game with two neural network-based
agents, we analyze the object-symbol mapping trying to understand what kind of
strategy was used to develop the emergent language. We see that, when the
environment is uniformly distributed, the agents rely on a random subset of
features to describe the objects. When we modify the objects making one feature
non-uniformly distributed,the agents realize it is less informative and start
to ignore it, and, surprisingly, they make a better use of the remaining
features. This interesting result suggests that more natural, less uniformly
distributed environments might aid in spurring the emergence of better-behaved
languages.
