Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Agent Smith: towards an evolutionary rule-based agent for interactive dynamic games
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Dealing with noisy fitness in the design of a RTS game bot
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
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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.