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.

翻译：基于深度学习的智能体间涌现通信研究因其对语言学与人工智能的启示而受到广泛关注。然而，先前的研究多聚焦于感知导向环境下的涌现通信——迫使智能体描述图像或符号语境中的低级感知特征。本研究受经典人类推理测试（即瑞文渐进矩阵）启发，提出推理游戏这一认知导向环境，鼓励智能体推理并交流高级规则，而非感知低级语境。此外，我们提出：1）一套无偏数据集（名为rule-RAVEN）作为基准以避免过拟合；2）一种两阶段课程式智能体训练方法作为基线，使推理游戏中语境与语义双向漂移时能更稳定收敛。实验结果表明，在推理游戏中会产生语义稳定且具构成性的语言以解决推理问题。这种涌现语言能帮助智能体将提取的规则泛化至未见语境属性，并在不同语境属性乃至任务间实现迁移。