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 towards 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 gameplaying agents. Results show that a search-based generative approach can adapt character classes, levels, or both towards 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 towards desired outcomes.

中文翻译：

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基于搜索的程序内容生成的多面代理模型

本文提出了一个框架，用于通过评估其质量和互补性的代理模型来程序生成游戏的级别和规则集组件。代理模型将关卡和规则集元素作为输入，将游戏结果作为输出，从而构建了游戏三个不同方面之间的映射。使用此模型作为昂贵的游戏模拟的替代品，基于搜索的生成器可以根据目标游戏结果调整内容。本文以射击游戏为目标域，探讨了如何将玩家角色类的参数映射到关卡表示以及平衡和匹配持续时间的游戏性结果。代理模型建立在深度学习架构上，在大量随机生成的级别集上进行训练，来自游戏代理的类和模拟。结果表明，基于搜索的生成方法可以使角色类、级别或两者都适应设计者指定的目标。因此，该模型可以充当设计助手或集成到混合倡议工具中。最重要的是，将三个游戏方面结合到模型中，使其能够识别关卡、规则和游戏玩法之间的协同作用，并协调前两者的生成以实现预期结果。