Real-Time Evolution of Neural Networks in the NERO Video Game (2006)
A major goal for AI is to allow users to interact with agents that learn in real time, making new kinds of interactive simulations, training applications, and digital entertainment possible. This paper describes such a learning technology, called real-time NeuroEvolution of Augmenting Topologies (rtNEAT), and describes how rtNEAT was used to build the NeuroEvolving Robotic Operatives (NERO) video game. This game represents a new genre of machine learning games where the player trains agents in real time to perform challenging tasks in a virtual environment. Providing laymen the capability to effectively train agents in real time with no prior knowledge of AI or machine learning has broad implications, both in promoting the field of AI and making its achievements accessible to the public at large.
In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-2006), pp. 1671--1674, Boston, MA 2006. Meno Park, CA: AAAI Press.

Bobby D. Bryant Ph.D. Alumni bdbryant [at] cse unr edu
Igor V. Karpov Masters Alumni ikarpov [at] gmail com
Risto Miikkulainen Faculty risto [at] cs utexas edu
Kenneth Stanley Postdoctoral Alumni kstanley [at] cs ucf edu