Abstract
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.
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Mora, A.M. et al. (2013). Designing and Evolving an Unreal TournamentTM 2004 Expert Bot. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_34
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DOI: https://doi.org/10.1007/978-3-642-38682-4_34
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