In standard neuro-evolution, the objective is to evolve a network that best handles a given task. Although this approach is useful for static tasks, it does not work well in real-time domains where the environment (and therefore the task) can vary. Furthermore, if the real-time domain is interactive, the task is unpredictable because the user can change his/her behavior at will. We have tackled this problem by introducing a method for real-time interactive neuro-evolution, and testing the method through a real-time interactive gaming scenario. As the environment changes, the population evolves along with it and can cope with the task. We show that this method is superior to standard neuro-evolution techniques in the paper below. Please see the Animated Demo.

Adrian Agogino is also a member of this project.