Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Human-Level AI's Killer Application: Interactive Computer Games
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Neuroevolution of an automobile crash warning system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A modular parametric architecture for the TORCS racing engine
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Optimized sensory-motor couplings plus strategy extensions for the TORCS car racing challenge
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Neural networks training for weapon selection in first-person shooter games
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
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In this paper a novel method for car racing controller learning is proposed. Car racing simulation is an active research field where new advances in aerodynamics, consumption and engine power are modelled and tested. The proposed approach is based on Neural Networks that learn the driving behaviour of other rule-based bots. Additionally, the resulted neural-networks controllers are evolved in order to adapt and increase their performance to a given racing track using genetic algorithms. The proposed bots are implemented and tested on several tracks of the open racing car simulator (TORCS) providing smoother driving behaviour than the corresponding rule-based bots and increased performance using the evolutionary adaptation.