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.

现有关于基于深度学习代理之间紧急通信的研究受到广泛关注，因为它对语言学和人工智能有启发作用。本研究提出了一个认知导向的环境 —— 推理游戏，鼓励代理人进行高层次规则的推理和沟通，而不是感知低层次环境。实验结果表明，在推理游戏中，出现了一个语义稳定且组合性强的语言来解决推理问题。这种演化的语言帮助代理人将提取的规则应用到看不见的环境属性的推广中，以及不同环境属性甚至任务之间的迁移。