Interactively Evolved Modular Neural Networks for Agent Control

  • Jessica C. Sparksa, Roberto Miguezb, John Reederc, Michael Georgiopoulosd
  • Published 2008


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 AI’s 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, co-evolution training technique to evolve adaptable agents with life like behaviors. Experiments conducted produced very promising results regarding efficiency of the technique, and demonstrate potential avenues for future research.

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Cite this paper

@inproceedings{Sparksa2008InteractivelyEM, title={Interactively Evolved Modular Neural Networks for Agent Control}, author={Jessica C. Sparksa and Roberto Miguezb and John Reederc and Michael Georgiopoulosd}, year={2008} }