Constrained surprise search for content generation

Daniele Gravina, Antonios Liapis, Georgios N. Yannakakis
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
more » ... y balanced and effective while also rewarding unexpected uses of these weapons during game simulations with artificial agents. Results in the paper demonstrate that searching for surprise can create functionally diverse weapons which require new gameplay patterns of weapon use in the game.
doi:10.1109/cig.2016.7860408 dblp:conf/cig/GravinaLY16 fatcat:bfleof2sc5f2pb5if3ziq5vfni