  To build agents that can collaborate effectively with others, recent research
has trained artificial agents to communicate with each other in Lewis-style
referential games. However, this often leads to successful but uninterpretable
communication. We argue that this is due to the game objective: communicating
about a single object in a shared visual context is prone to overfitting and
does not encourage language useful beyond concrete reference. In contrast,
human language conveys a rich variety of abstract ideas. To promote such
skills, we propose games that require communicating generalizations over sets
of objects representing abstract visual concepts, optionally with separate
contexts for each agent. We find that these games greatly improve systematicity
and interpretability of the learned languages, according to several metrics in
the literature. Finally, we propose a method for identifying logical operations
embedded in the emergent languages by learning an approximate compositional
reconstruction of the language.
