Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Exploiting Intelligence in Fighting Action Games Using Neural Networks
IEICE - Transactions on Information and Systems
Agent Smith: towards an evolutionary rule-based agent for interactive dynamic games
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Genetic evolution of fuzzy finite state machines to control bots in a first-person shooter game
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolving behaviour trees for the Mario AI competition using grammatical evolution
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Decision tree-based algorithms for implementing bot AI in UT2004
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Game designers training first person shooter bots
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Designing and evolving an unreal Tournament™ 2004 expert bot
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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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.