Adaptability and Diversity in Simulated Turn-taking Behaviour

Abstract

Turn-taking behaviour is simulated in a coupled agents system. Each agent is modelled as a mobile robot with two wheels. A recurrent neural network is used to produce the motor outputs and to hold the internal dynamics. Agents are developed to take turns on a two-dimensional arena by causing the network structures to evolve. Turn-taking is established using either regular or chaotic behaviour of the agents. It is found that chaotic turn-takers are more sensitive to the adaptive inputs from the other agent. Conversely, regular turn-takers are comparatively robust against noisy inputs, owing to their restricted dynamics. From many observations, including turn-taking with virtual agents, we claim that there is a complementary relationship between robustness and adaptability. Furthermore, by investigating the recoupling of agents from different GA generations, we report the emergence of a new turn-taking behaviour. Potential for synthesizing a new form of motion is another characteristic of chaotic turn-takers.