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.