The Procedural Content Generation Benchmark: An Open-source Testbed for Generative Challenges in Games
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909555765542912 |
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| author | Khalifa, Ahmed Gallotta, Roberto Barthet, Matthew Liapis, Antonios Togelius, Julian Yannakakis, Georgios N. |
| author_facet | Khalifa, Ahmed Gallotta, Roberto Barthet, Matthew Liapis, Antonios Togelius, Julian Yannakakis, Georgios N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21474 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Procedural Content Generation Benchmark: An Open-source Testbed for Generative Challenges in Games Khalifa, Ahmed Gallotta, Roberto Barthet, Matthew Liapis, Antonios Togelius, Julian Yannakakis, Georgios N. Artificial Intelligence Machine Learning 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. |
| title | The Procedural Content Generation Benchmark: An Open-source Testbed for Generative Challenges in Games |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2503.21474 |