Designing and implementing the decisions of Non-Player Characters in first person shooter games becomes more difficult as the games get more complex. For every additional feature in a level potentially all decisions have to be revisited and another check made on this new feature. This leads to an explosion of the number of cases that have to be checked, which in its turn leads to situations where combinations of features are overlooked and Non-Player Characters act strange in those particular circumstances. In this paper we show how evolutionary neural networks can be used to avoid these problems and lead to good and robust behavior.
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