Crossover and mutation operators for grammar-guided genetic programming
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Modeling player experience in super mario bros
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Evolving content in the galactic arms race video game
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Evolutionary construction and adaptation of intelligent systems
Expert Systems with Applications: An International Journal
Evolving board-game players with genetic programming
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Grammar-guided evolutionary construction of bayesian networks
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Search-based procedural content generation
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
GP-Gammon: using genetic programming to evolve backgammon players
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
GP-Robocode: using genetic programming to evolve robocode players
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
A card game description language
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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A grammar-guided genetic program is presented to automatically build and evolve populations of AI controlled enemies in a 2D third-person shooter called Genes of War. This evolutionary system constantly adapts enemy behaviour, encoded by a multi-layered fuzzy control system, while the game is being played. Thus the enemy behaviour fits a target challenge level for the purpose of maximizing player satisfaction. Two different methods to calculate this challenge level are presented: "hardwired" that allows the desired difficulty level to be programed at every stage of the gameplay, and "adaptive" that automatically determines difficulty by analyzing several features extracted from the player's gameplay. Results show that the genetic program successfully adapts armies of ten enemies to different kinds of players and difficulty distributions.