Controlling bots in a First Person Shooter Game using Genetic Algorithms

In this paper we employ a steady state genetic algorithm to evolve different types of behaviour for bots in the Unreal Tournament 2004 (TM) computer game. For this purpose we define three fitness functions which are based on the number of enemies killed, the lifespan of the bot and a combination of both. Long run experiments were carried out, in which the evolved bots' behaviours outperform those of standard bots supplied by the game, particularly in those cases where the fitness involves a measure of the bot's lifespan. Also, there is an increase in the number of items collected, and the behaviours tend to become less aggressive, tending instead towards a more optimised combat style. Further "short run" experiments were carried out with a further type of fitness function defined, based on the number of items picked. In these cases the bots evolve performances towards the goal they have been aimed, with no other behaviours arising, except in the case of the multiple objective one. We conclude that in order to evolve interesting behaviours more complex fitness functions are needed, and not necessarily ones that directly include the goal we are aiming for.