Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
An Behavior-based Robotics
Evolution of a subsumption architecture neurocontroller
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - AILS '04
Robust player imitation using multiobjective evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
LSTM recurrent networks learn simple context-free and context-sensitive languages
IEEE Transactions on Neural Networks
Evolving agent behavior in multiobjective domains using fitness-based shaping
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolving interesting maps for a first person shooter
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary 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
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We describe the architecture of a hierarchical learning-based controller for bots in the First-Person Shooter (FPS) game Unreal Tournament 2004. The controller is inspired by the subsumption architecture commonly used in behaviour-based robotics. A behaviour selector decides which of three sub-controllers gets to control the bot at each time step. Each controller is implemented as a recurrent neural network, and trained with artificial evolution to perform respectively combat, exploration and path following. The behaviour selector is trained with a multiobjective evolutionary algorithm to achieve an effective balancing of the lower-level behaviours. We argue that FPS games provide good environments for studying the learning of complex behaviours, and that the methods proposed here can help developing interesting opponents for games.