Award
Our research article
on NERO won the Best Paper Award at the IEEE Symposium
on Computational Intelligence and Games 2005.
Abstract:
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 during gameplay, 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 a new genre of video games in which the player teaches a team of
agents through a series of customized training exercises. In order to
demonstrate this concept in the NeuroEvolvingRoboticOperatives (NERO)
game, the player trains a team of robots for combat. This paper describes
results from this novel application of machine learning, and also
demonstrates how multiple agents can evolve and adapt in video games like
NERO in real time using rtNEAT. In the future, rtNEAT may allow new kinds
of educational and training applications that adapt online as the user
gains new skills.
Photo of Award
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TV News Coverage
KXAN-TV 36 News "UT Labs Developing Smarter Video Games"
Follow the link and click the small camera icon at the top of the story in
order to view the video.
If you're like most of us, you'd probably never associate a video game
with car safety or rocket science. New research at the University of Texas
could change your mind while it changes the future of everything from
driving to gaming.
"If you're like most of us, you'd probably
never associate a video game with car safety or rocket science. New
research at the University of Texas could change your mind while it
changes the future of everything from driving to gaming."
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