This paper introduces a methodology through which a population of autonomous agents can establish a linguistic convention that enables them to refer to arbitrary entities that they observe in their environment. The linguistic convention emerges in a decentralised manner through local communicative interactions between pairs of agents drawn from the population. The convention consists of symbolic labels (word forms) associated to concept representations (word meanings) that are grounded in a continuous feature space. The concept representations of each agent are individually constructed yet compatible on a communicative level. Through a range of experiments, we show (i) that the methodology enables a population to converge on a communicatively effective, coherent and human-interpretable linguistic convention, (ii) that it is naturally robust against sensor defects in individual agents, (iii) that it can effectively deal with noisy observations, uncalibrated sensors and heteromorphic populations, (iv) that the method is adequate for continual learning, and (v) that the convention self-adapts to changes in the environment and communicative needs of the agents.

翻译：本文提出一种方法论，使得一组自主智能体能够建立一种语言惯例，使其能够指代环境中观察到的任意实体。该语言惯例通过种群中成对智能体间的局部交际互动以去中心化方式涌现。惯例由与概念表征（词义）关联的符号标签（词形）构成，且这些概念表征扎根于连续特征空间。每个智能体的概念表征虽独立构建，却在交际层面相互兼容。通过一系列实验，我们表明：（i）该方法论能使种群收敛至交际高效、连贯且人类可解释的语言惯例；（ii）该方法对个体智能体的传感器缺陷具有天然鲁棒性；（iii）它能有效处理噪声观测、未校准传感器及异形种群；（iv）该方法适用于持续学习；（v）该惯例能自适应环境变化及智能体的交际需求。