首个游戏生成内容评估基准，可标准化测试算法表现。
The Procedural Content Generation Benchmark: An Open-source Testbed for Generative Challenges in Games
- 构建12类游戏内容生成任务，含多种变体和评价指标。
- 三种基线算法在不同任务中表现差异显著，目标影响生成质量与多样性。
- 适合研究生成算法的评估、游戏开发与算法对比者使用。
本文提出游戏过程化内容生成基准（Procedural Content Generation Benchmark），用于评估生成算法在各类游戏内容创作任务中的表现。该基准包含12个游戏相关问题，每个问题有多个变体，涵盖不同类型的关卡生成及简单街机游戏规则生成等任务。每项任务均配有专属内容表示、控制参数和质量、多样性、可控性评价指标。本基准旨在推动生成算法评估的标准化。我们用该基准评测了三种基线算法：随机生成器、进化策略和遗传算法。结果表明，不同任务难度差异明显，且所选目标对生成内容的质量、多样性与可控性有显著影响。
原文摘要 · Abstract (English)
This paper introduces the Procedural Content Generation Benchmark for evaluating generative algorithms on different game content creation tasks. The benchmark comes with 12 game-related problems with multiple variants on each problem. Problems vary from creating levels of different kinds to creating rule sets for simple arcade games. Each problem has its own content representation, control parameters, and evaluation metrics for quality, diversity, and controllability. This benchmark is intended as a first step towards a standardized way of comparing generative algorithms. We use the benchmark to score three baseline algorithms: a random generator, an evolution strategy, and a genetic algorithm. Results show that some problems are easier to solve than others, as well as the impact the chosen objective has on quality, diversity, and controllability of the generated artifacts.
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