Please note: This is a version of the paper that appears in IEEE Transactions on Evolutionary Computation. For the journal-formatted version, please visit the IEEE site.
In most modern video games, character behavior
is scripted; no matter how many times the player exploits a
weakness, that weakness is never repaired. Yet, if game characters
could learn through interacting with the player, behavior
could improve as the game is played, keeping it interesting. This
paper introduces the real-time Neuroevolution of Augmenting
Topologies (rtNEAT) method for evolving increasingly complex
artificial neural networks in real time, as a game is being played.
The rtNEAT method allows agents to change and improve during
the game. In fact, rtNEAT makes possible an entirely new genre of
video games in which the player trains a team of agents through
a series of customized exercises. To demonstrate this concept, the
Neuroevolving Robotic Operatives (NERO) game was built based
on rtNEAT. In NERO, the player trains a team of virtual robots for
combat against other players?’ teams. This paper describes results
from this novel application of machine learning, and demonstrates
that rtNEAT makes possible video games like NERO where
agents evolve and adapt in real time. In the future, rtNEAT may
allow new kinds of educational and training applications through
interactive and adapting games.