This presentation describes the design of a bot for the first person shooter Unreal Tournament 2004, which behaves as a human expert player in 1 vs. 1 death matches. This has been implemented modelling the actions (and tricks) of this player, using a state-based AI, and supplemented by a database for ‘learning’ the arena. The expert bot yields excellent results, beating the game default bots in the hardest difficulty, and even being a very hard opponent for the human players (including our expert). The AI of this bot is then improved by means of three different approaches of evolutionary algorithms, optimizing a wide set of parameters (weights and probabilities) which the expert bot considers when playing. The result of this process yields an even better rival; however the noisy nature of the fitness function (due to the pseudostochasticity of the battles) makes the evolution slower than usual.