Emergent Language: A Survey and Taxonomy (link.springer.com)

A recent survey titled "Emergent Language: A Survey and Taxonomy" sheds light on the burgeoning field of emergent language (EL) research within artificial intelligence, especially in the context of multi-agent reinforcement learning (MARL). This comprehensive review aims to define key terminologies, evaluate existing methods and metrics, and identify research gaps in the development of autonomous communication systems. Unlike traditional approaches that mimic human language formation, the study focuses on enabling AI agents to develop their own communication forms that can facilitate complex interactions, including human-to-agent communication in natural language (NL). The significance of this research lies in its potential to enhance the communicative capabilities of AI agents, thereby moving toward more human-centric AI applications. By providing a taxonomy and structured metrics for evaluating emergent communication strategies, this work addresses critical issues in the growing field, such as the conditions necessary for language emergence and the effectiveness of various communication strategies. As AI agents become more adept at independent communication, insights gained from this research could lead to revolutionary advancements in collaborative AI systems, ultimately fostering more effective interactions between humans and machines.