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Constrained surprise search for content generation
2016
2016 IEEE Conference on Computational Intelligence and Games (CIG)
In procedural content generation, it is often desirable to create artifacts which not only fulfill certain playability constraints but are also able to surprise the player with unexpected potential uses. This paper applies a divergent evolutionary search method based on surprise to the constrained problem of generating balanced and efficient sets of weapons for the Unreal Tournament III shooter game. The proposed constrained surprise search algorithm ensures that pairs of weapons are
doi:10.1109/cig.2016.7860408
dblp:conf/cig/GravinaLY16
fatcat:bfleof2sc5f2pb5if3ziq5vfni