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Title: Constrained surprise search for content generation
Authors: Gravina, Daniele
Liapis, Antonios
Yannakakis, Georgios N.
Keywords: Computer games -- Surprise
Computer games -- Design
Genetic algorithms
Issue Date: 2016-09
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Gravina, D., Liapis, A., & Yannakakis, G. N. (2016). Constrained surprise search for content generation. IEEE Conference on Computational Intelligence and Games (CIG), 2016, Santorini.
Abstract: 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 sufficiently 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.
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