This work describes the design of a bot for the first person shooter Unreal TournamentTM 2004 (UT2K4), 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 IA, 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 pseudo-stochasticity of the battles) makes the evolution slower than usual. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Mora, A. M., Aisa, F., Caballero, R., García-Sánchez, P., Julián Merelo, J., Castillo, P. A., & Lara-Cabrera, R. (2013). Designing and evolving an unreal tournamentTM 2004 expert bot. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7903 LNCS, pp. 312–323). https://doi.org/10.1007/978-3-642-38682-4_34
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