Abstract
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.
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Priesterjahn, S., Kramer, O., Weimer, A., Goebels, A. (2005). Evolution of Reactive Rules in Multi Player Computer Games Based on Imitation. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_105
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DOI: https://doi.org/10.1007/11539117_105
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