genetic evolution of fuzzy finite state machines to control bots in a first-person shooter game
In this work we employ a steady state genetic algorithm to evolve bots' behaviors in the Unreal Tournament 2004 game. Our aim is to show whether interesting behaviors can be obtained with simple fitness functions. For this purpose we define four functions, measuring the number of enemies killed, the bot's lifespan, a combination of both and the number of items collected. The experiments show that incorporating a measure of the bot's lifespan in the fitness results in an optimal behavior in all aspects considered; further, the bots evolved this way outperform the standard bots supplied by the game. In addition, there is an increase in the number of items collected (even when this is not explicitly included in the fitness) and a tendency towards a more optimised combat style with less aggressive behaviors.
fuzzy finite state machines genetic algorithms autonomous agents videogames