Interactively evolved modular neural networks for game agent control

As the realism in games continues to increase, through improvements in graphics and 3D engines, more focus is placed on the behavior of the simulated agents that inhabit the simulated worlds. The agents in modern video games must become more life-like in order to seem to belong in the environments they are portrayed in. Many modern artificial intelligence approaches achieve a high level of realism but this is accomplished through significant developer time spent scripting the behaviors of the non-playable characters or NPC´s. These agents will behave in a believable fashion in the scenarios they have been programmed for, but do not have the ability to adapt to new situations. In this paper we introduce a modularized, real-time evolution training technique to evolve adaptable agents with life-like behaviors. Online performance during evolution is also improved by using selection mechanisms found in temporal difference learning methods to appropriately balance the exploration and exploitation of control policies. These methods are implemented and tested using the XNA framework producing very promising results regarding efficiency of techniques, and demonstrating many potential avenues for further research.