The field of emergent language represents a novel area of research within the domain of artificial intelligence, particularly within the context of multi-agent reinforcement learning. Although the concept of studying language emergence is not new, early approaches were primarily concerned with explaining human language formation, with little consideration given to its potential utility for artificial agents. In contrast, studies based on reinforcement learning aim to develop communicative capabilities in agents that are comparable to or even superior to human language. Thus, they extend beyond the learned statistical representations that are common in natural language processing research. This gives rise to a number of fundamental questions, from the prerequisites for language emergence to the criteria for measuring its success. This paper addresses these questions by providing a comprehensive review of 181 scientific publications on emergent language in artificial intelligence. Its objective is to serve as a reference for researchers interested in or proficient in the field. Consequently, the main contributions are the definition and overview of the prevailing terminology, the analysis of existing evaluation methods and metrics, and the description of the identified research gaps.

翻译：涌现语言领域代表了人工智能研究中的一个新兴方向，尤其在多智能体强化学习背景下。尽管研究语言涌现现象的概念并非全新，早期方法主要关注解释人类语言的形成，而很少考虑其对人工智能体的潜在效用。相比之下，基于强化学习的研究旨在开发智能体的交流能力，使其达到甚至超越人类语言水平。因此，这些研究超越了自然语言处理领域中常见的学习统计表征范畴。这引发了一系列基础性问题，从语言涌现的前提条件到衡量其成功的标准。本文通过对人工智能领域中181篇涌现语言相关科学文献的系统性综述来探讨这些问题，旨在为对该领域感兴趣或精通的学者提供参考。因此，本文的主要贡献包括：对主流术语的定义与梳理、对现有评估方法与指标的分析，以及对已识别研究空白的阐述。