<lmargin%:5><rmargin%:95><font:Arial Bold:20>AI Technology in NERO - rtNEAT
<font:Arial:14>While this section is not necessary to play NERO, you may be wondering how the robots in NERO learn.  This section briefly explains the technology behind NERO and points you to more detailed references.

A major goal of this game was to showcase how a novel machine learning technology can impact the video games industry.  The technology in the game is called real-time NeuroEvolution of Augmenting Topologies (rtNEAT for short).  rtNEAT is a real-time genetic algorithm; that is, a population of artificial neural networks are evolved by evaluating their performance and allowing the better ones to reproduce through crossover while eliminating the worse ones.  Over time, the process of Darwinian natural selection (survival of the fittest) yields better and better performers.

rtNEAT is the a real-time variant of NEAT, which was originally introduced in 2001 to evolve increasingly complex neural network topologies for difficult control problems.  After several years of successful agent control applications, NEAT was a natural choice for controlling video game characters.  To make this possible, the algorithm was altered to make it real-time, enabling the implementation of NERO.  rtNEAT can potentially supply the intelligence for agents in a variety of game and training genres.

A great deal of information is available on NEAT over the web.  The original journal article in Evolutionary Computation is a good introduction:

http://nn.cs.utexas.edu/keyword?stanley:ec02

A shorter award-winning conference-paper version is a faster read:

http://nn.cs.utexas.edu/keyword?stanley:gecco02b

This journal article provides more details on NEAT used to control mobile autonomous agents, a precursor to NERO:

http://nn.cs.utexas.edu/keyword?stanley:jair04

NEAT has its own Users Page, which includes pointers to available software and a FAQ:

http://www.cs.utexas.edu/users/kstanley/neat.html

A NEAT Users Group is also available:

http://groups.yahoo.com/group/neat/

In 2000, Agogino, Stanley, and Miikkulainen published a paper on real-time neuroevolution. While this paper preceded NEAT, it was a major inspiration and proof-of-concept for eventually making NEAT real-time, and the game of NERO itself:

http://nn.cs.utexas.edu/keyword?agogino:npl00

rtNEAT is described in detail in the following paper, which won the Best Paper Award at the IEEE 2005 Symposium on Computational Intelligence and Games:

http://nn.cs.utexas.edu/keyword?stanley:cig05

Milestoning experiments (using the milestone option) and the associated algorithm are described in this paper from the first Artificial Intelligence and Interactive Digital Interactive Entertainment (AIIDE-2005) conference:

http://nn.cs.utexas.edu/keyword?dsilva:aiide05

Project NERO has its own special academic research website with links to publications and researchers:

http://nn.cs.utexas.edu/keyword?nero

Lastly, a lot of information can be found on Ken Stanley’s website:

http://www.cs.utexas.edu/users/kstanley/

