  Research on emergent communication between deep-learning-based agents has
received extensive attention due to its inspiration for linguistics and
artificial intelligence. However, previous attempts have hovered around
emerging communication under perception-oriented environmental settings, that
forces agents to describe low-level perceptual features intra image or symbol
contexts. In this work, inspired by the classic human reasoning test (namely
Raven's Progressive Matrix), we propose the Reasoning Game, a
cognition-oriented environment that encourages agents to reason and communicate
high-level rules, rather than perceived low-level contexts. Moreover, we
propose 1) an unbiased dataset (namely rule-RAVEN) as a benchmark to avoid
overfitting, 2) and a two-stage curriculum agent training method as a baseline
for more stable convergence in the Reasoning Game, where contexts and semantics
are bilaterally drifting. Experimental results show that, in the Reasoning
Game, a semantically stable and compositional language emerges to solve
reasoning problems. The emerged language helps agents apply the extracted rules
to the generalization of unseen context attributes, and to the transfer between
different context attributes or even tasks.
