Decentralised Emergence of Robust and Adaptive Linguistic Conventions in Populations of Autonomous Agents Grounded in Continuous Worlds

The field of emergent communication investigates how populations of agents can learn to communicate to solve tasks cooperatively. This paper introduces a methodology for establishing linguistic conventions within populations of autonomous agents in a fully decentralised manner. These conventions enable agents to refer to arbitrary entities in their environment. In our approach, agents participate in reference-based language games, wherein agents aim to direct each other’s attention to specific entities in their shared environment by using linguistic conventions. Importantly, our methodology is directly applicable to any dataset that describes entities using continuously valued features. Through repeated local communicative interactions, agents gradually build up a linguistic inventory, associating words with conceptual representations. We validate our methodology through six experiments on three large tabular datasets, demonstrating its effectiveness in facilitating the emergence of linguistic conventions within populations of autonomous agents. Furthermore, the experiments showcase the robustness of the methodology against sensor defects, its ability to handle noise observations and uncalibrated sensors, suitability for continual learning, and capacity to foster self-adapting languages in response to environmental changes and communicative needs.