Emergence of Writing Systems Through Multi-Agent Cooperation
This work addresses the challenge of developing communication in AI, specifically for multi-agent systems, but is incremental as it builds on existing language evolution studies.
The paper tackles the problem of emergent writing systems in artificial agents by introducing a referential game setup where two agents learn to coordinate using a written language, and demonstrates successful coordination while analyzing how game rules affect writing system taxonomy with a consistency metric.
Learning to communicate is considered an essential task to develop a general AI. While recent literature in language evolution has studied emergent language through discrete or continuous message symbols, there has been little work in the emergence of writing systems in artificial agents. In this paper, we present a referential game setup with two agents, where the mode of communication is a written language system that emerges during the play. We show that the agents can learn to coordinate successfully using this mode of communication. Further, we study how the game rules affect the writing system taxonomy by proposing a consistency metric.