Many games are reliant on creating new and engaging content constantly to maintain the interest of their player-base. One such example are puzzle games, in such it is common to have a recurrent need to create new puzzles. Creating new puzzles requires guaranteeing that they are solvable and interesting to players, both of which require significant time from the designers. Automatic validation of puzzles provides designers with a significant time saving and potential boost in quality. Automation allows puzzle designers to estimate different properties, increase the variety of constraints, and even personalize puzzles to specific players. Puzzles often have a large design space, which renders exhaustive search approaches infeasible, if they require significant time. Specifically, those puzzles can be formulated as quadratic combinatorial optimization problems. This paper presents an evolutionary algorithm, empowered by expert-knowledge informed heuristics, for solving logical puzzles in video games efficiently, leading to a more efficient design process. We discuss multiple variations of hybrid genetic approaches for constraint satisfaction problems that allow us to find a diverse set of near-optimal solutions for puzzles. We demonstrate our approach on a fantasy Party Building Puzzle game, and discuss how it can be applied more broadly to other puzzles to guide designers in their creative process.

翻译：许多游戏依赖持续创作新颖且吸引人的内容来维持玩家群体的兴趣。谜题类游戏便是典型例子：这类游戏常需要反复生成新谜题。创作新谜题需要确保其可解性及对玩家的吸引力，这两点均需投入设计师大量时间。谜题的自动验证机制能为设计师显著节省时间，并有可能提升设计质量。自动化使得谜题设计师能够评估不同属性、增加约束多样性，甚至针对特定玩家进行个性化谜题设计。谜题的设计空间通常非常广阔，若求解耗时较长，穷举搜索方法便不可行。具体而言，这类谜题可被形式化为二次组合优化问题。本文提出一种融合专家知识启发式规则的进化算法，用于高效求解电子游戏中的逻辑谜题，从而优化设计流程。我们探讨了针对约束满足问题的多种混合遗传算法变体，这些方法能够为谜题找到多样化的近优解。通过一款奇幻主题的组队编排谜题游戏验证了该方法的有效性，并讨论了如何将其更广泛地应用于其他谜题类型，以引导设计师的创作过程。