A team from the University of Texas at Austin have devised a new kind of computer game where the player teaches the computer rather than reacting to it.

Using a technique known as real-time enhancement of the NeuroEvolution of Augmenting Topologies (rtNEAT), computers can be induced to learn good and bad gameplay behaviour and players can instruct characters how to react to certain stimuli.

The final game, dubbed the NeuroEvolving Robotic Operatives (Nero) project, locks the learning process so that players cannot automatically learn tactics taught by other players.

A team of 30 engineers, designers and gamers have been working on the project for two years and their paper, Evolving Neural Network Agents in the Nero Video Game, won the best paper award at the IEEE 2005 Symposium on Computational Intelligence and Games conference this year. 

"In most modern video games, character behaviour is scripted; no matter how many times the player exploits a weakness, that weakness is never repaired," said the paper.

"In Nero, the player takes the role of a trainer and constructs training scenarios for a team of simulated robots.

"The rtNEAT technique can form the basis for other similar interactive learning applications in the future, and eventually even make it possible to use gaming as a method for training people in sophisticated tasks."

Training begins with 50 robots in an arena controlled by random neural networks. As the training progresses these organise into specific roles. The player can then submit his team for battle over the internet or train them in new skills.

In the first test of the game last year it took about 100 seconds to train 90 per cent of 'soldiers' to run at the enemy. When teaching soldiers to run away the computer discovered for itself that running away backwards was preferable since it allowed the opportunity to return fire.

The team have not announced a date for any commercial release, but hope to use the technology to improve educational software as well as games.