将游戏机制升维为可生成的坐标,让关卡设计更可控、可验证。
High Dimensional Procedural Content Generation

- 把玩法维度如时间、平行世界等纳入统一空间生成
- 在4D空间实现重力反转等复杂机制的可达性验证
- 适合想构建可调控、可扩展关卡生成系统的开发者
程序化内容生成(PCG)在静态2D/3D几何生成上已取得显著进展,但多数方法将游戏机制视为附属,仅在空间维度优化。本文提出高维程序化内容生成(HDPCG),将非几何玩法维度提升为联合状态空间的一等坐标。沿两个方向实现:方向-空间在几何中加入离散层维度,验证4D(x,y,z,l)空间中的可达性,统一处理2.5D/3.5D机制如重力反转与平行世界切换;方向-时间通过时间展开图引入动态时间维度,捕捉动作语义与冲突规则。每个方向提供三种通用、可实践算法,共享抽象骨架生成、受控落地、高维验证及多指标评估的流程。大规模实验在多种场景下验证了问题定义的完整性与方法在可玩性、结构、风格、鲁棒性与效率上的有效性。基于Unity的案例研究重现了符合度量标准的可玩场景。希望HDPCG推动PCG向通用表示演进,生成超越几何的游戏相关维度,为可控、可验证、可扩展的关卡生成铺路。
原文摘要 · Abstract (English)
Procedural content generation (PCG) has made substantial progress in shaping static 2D/3D geometry, while most methods treat gameplay mechanics as auxiliary and optimize only over space. We argue that this limits controllability and expressivity, and formally introduce High-Dimensional PCG (HDPCG): a framework that elevates non-geometric gameplay dimensions to first-class coordinates of a joint state space. We instantiate HDPCG along two concrete directions. Direction-Space augments geometry with a discrete layer dimension and validates reachability in 4D (x,y,z,l), enabling unified treatment of 2.5D/3.5D mechanics such as gravity inversion and parallel-world switching. Direction-Time augments geometry with temporal dynamics via time-expanded graphs, capturing action semantics and conflict rules. For each direction, we present three general, practicable algorithms with a shared pipeline of abstract skeleton generation, controlled grounding, high-dimensional validation, and multi-metric evaluation. Large-scale experiments across diverse settings validate the integrity of our problem formulation and the effectiveness of our methods on playability, structure, style, robustness, and efficiency. Beyond quantitative results, Unity-based case studies recreate playable scenarios that accord with our metrics. We hope HDPCG encourages a shift in PCG toward general representations and the generation of gameplay-relevant dimensions beyond geometry, paving the way for controllable, verifiable, and extensible level generation.
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