Abstract
This paper describes the design, implementation and results of an evolutionary bot inside the PC game UnrealTM, that is, an autonomous enemy which tries to beat the human player and/or some other bots. The default artificial intelligence (AI) of this bot has been improved using two different evolutionary methods: genetic algorithms (GAs) and genetic programming (GP). The first one has been applied for tuning the parameters of the hard-coded values inside the bot AI code. The second method has been used to change the default set of rules (or states) that defines its behaviour. Both techniques yield very good results, evolving bots which are capable to beat the default ones. The best results are yielded for the GA approach, since it just does a refinement following the default behaviour rules, while the GP method has to redefine the whole set of rules, so it is harder to get good results.
Supported in part by the MICYT projects NoHNES (TIN2007-68083) and TIN2008-06491-C04-01, and the Junta de Andalucía (P06-TIC-02025 and P07-TIC-03044).
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Mora, A.M. et al. (2010). Evolving Bot AI in UnrealTM . In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12239-2_18
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DOI: https://doi.org/10.1007/978-3-642-12239-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12238-5
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