Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Digital Image Processing
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Capturing the quake player: using a BDI agent to model human behaviour
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
An evolutionary online adaptation method for modern computer games based on imitation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Imitation-based evolution of artificial players in modern computer games
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Real-time imitation-based adaptation of gaming behaviour in modern computer games
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Imitation-based evolution of artificial game players
ACM SIGEVOlution
Programming Robosoccer agents by modeling human behavior
Expert Systems with Applications: An International Journal
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Learning drivers for TORCS through imitation using supervised methods
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
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Observing purely reactive situations in modern computer games, one can see that in many cases few, simple rules are sufficient to perform well in the game. In spite of this, the programming of an artificial opponent is still a hard and time consuming task in the way it is done for the most games today. In this paper we propose a system in which no direct programming of the behaviour of the opponents is necessary. Instead, rules are gained by observing human players and then evaluated and optimised by an evolutionary algorithm to optimise the behaviour. We will show that only little learning effort is required to be competitive in reactive situations. In the course of our experiments our system proved to generate better artificial players than the original ones supplied with the game.