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Research in multi-agent cooperation has shown that artificial agents are ableto 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 anatural language. In a simple referential game with two neural network-basedagents, we analyze the object-symbol mapping trying to understand what kind ofstrategy was used to develop the emergent language. We see that, when theenvironment is uniformly distributed, the agents rely on a random subset offeatures to describe the objects. When we modify the objects making one featurenon-uniformly distributed,the agents realize it is less informative and startto ignore it, and, surprisingly, they make a better use of the remainingfeatures. This interesting result suggests that more natural, less uniformlydistributed environments might aid in spurring the emergence of better-behavedlanguages.

Focus on What's Informative and Ignore What's not: Communication Strategies in a Referential Game