A Signal-Centric Perspective on the Evolution of Symbolic Communication

The evolution of symbolic communication is a longstanding open research question in biology. While some theories suggest that it originated from sub-symbolic communication (i.e., iconic or indexical), little experimental evidence exists on how organisms can actually evolve to define a shared set of symbols with unique interpretable meaning, thus being capable of encoding and decoding discrete information. Here, we use a simple synthetic model composed of sender and receiver agents controlled by Continuous-Time Recurrent Neural Networks, which are optimized by means of neuro-evolution. We characterize signal decoding as either regression or classification, with limited and unlimited signal amplitude. First, we show how this choice affects the complexity of the evolutionary search, and leads to different levels of generalization. We then assess the effect of noise, and test the evolved signaling system in a referential game. In various settings, we observe agents evolving to share a dictionary of symbols, with each symbol spontaneously associated to a 1-D unique signal. Finally, we analyze the constellation of signals associated to the evolved signaling systems and note that in most cases these resemble a Pulse Amplitude Modulation system.Comment: To be published in the proceedings of ACM Genetic and Evolutionary Computation Conference (GECCO) 202

Analysis of Evolved Agents Performing Referential Communication

A pair of Continuous-time Recurrent Neural Network (CTRNN) based agents called “Sender ” and “Receiver ” is evolved on a circular world. Their collective objective is to communicate and move to a target – the Sender needs to communicate the address of a target location on the circle, and the Receiver needs to move to that location after receiving the communication. In extension of previous work (Williams and Beer, 2008), the agents are evolved under conditions different from the original work. Qualitative analysis of the most successful agent-pair shows that the Receiver’s behavior is reminiscent of Newton’s equations of motion in relating its initial velocity to the target address communicated to it. Further analysis using information-theoretic tools reveals a pair of neurons that hold crucial information required for th