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A Multifaceted Surrogate Model for Search-Based Procedural Content Generation
This paper proposes a framework for the procedural generation of level and ruleset components of games via a surrogate model that assesses their quality and complementarity. The surrogate model combines level and ruleset elements as input and gameplay outcomes as output, thus constructing a mapping between three different facets of games. Using this model as a surrogate for expensive gameplay simulations, a search-based generator can adapt content toward a target gameplay outcome. Using a shooter game as the target domain, this paper explores how parameters of the players' character classes can be mapped to both the level's representation and the gameplay outcomes of balance and match duration. The surrogate model is built on a deep learning architecture, trained on a large corpus of randomly generated sets of levels, classes, and simulations from game playing agents. Results show that a search-based generative approach can adapt character classes, levels, or both toward designer-specified targets. The model can thus act as a design assistant or be integrated in a mixed-initiative tool. Most importantly, the combination of three game facets into the model allows it to identify the synergies between levels, rules, and gameplay and orchestrate the generation of the former two toward desired outcomes.
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