Real-time interactive learning in the NERO video game

Authors:
Kenneth O. Stanley;Igor Karpov;Risto Miikkulainen;Aliza Gold
Affiliations:
School of Electrical Engineering and Computer Science, The University of Central Florida, Orlando, FL;Department of Computer Sciences, The University of Texas at Austin, Austin, TX;Department of Computer Sciences, The University of Texas at Austin, Austin, TX;Digital Media Collaboratory, IC²Institute, The University of Texas at Austin, Austin, TX
Venue:
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Year:
2006

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Abstract

In the NeuroEvolving Robotic Operatives (NERO) video game, the player trains a team of virtual robots for combat against other players' teams. The virtual robots learn in real time through interacting with the player. Since NERO was originally released in June, 2005, it has been downloaded over 50,000 times, appeared on Slashdot, and won several honors. The real-time NeuroEvolution of Augmenting Topologies (rt-NEAT) method, which can evolve increasingly complex artificial neural networks in real time as a game is being played, drives the robots' learning, making possible this entirely new genre of video game. The live demo will show how agents in NERO adapt in real time as they interact with the player. In the future, rtNEAT may allow new kinds of educational and training applications through interactive and adapting games.