  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.
