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：许多游戏依赖于持续创建新颖且引人入胜的内容来维持玩家群体的兴趣。拼图类游戏便是典型范例，这类游戏通常需要反复设计新谜题。创作新谜题需要确保其具备可解性和趣味性，这两项要求都会耗费设计师大量时间。自动化验证系统可为设计师显著节省时间成本，并提升游戏品质潜力。通过自动化技术，拼图设计师能够估算不同属性特征、增加约束条件的多样性，甚至为特定玩家定制个性化谜题。由于拼图的设计空间通常极为庞大，若采用穷举搜索方法会因耗时过长而难以实现，特别是那些可被建模为二次组合优化问题的谜题类型。本文提出一种融合专家知识启发式策略的进化算法，用于高效求解电子游戏中的逻辑谜题，从而优化设计流程。我们探讨了多种面向约束满足问题的混合遗传算法变体，这些方法能够为拼图问题找到多样化且近似最优的解集。通过一个奇幻题材的组队构建拼图游戏案例验证了方法的有效性，并进一步论述了如何将该方法推广至其他拼图类型以指导设计师的创意开发过程。