mlsanityData to 7 Oct 2026

Adapting Procedural Content Generation to Player Personas Through Evolution

arXiv 2112.04406 ↗First posted 7 Dec 2021cs.AI cs.HCcited by 2

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Abstract (arXiv)

Automatically adapting game content to players opens new doors for game development. In this paper we propose an architecture using persona agents and experience metrics, which enables evolving procedurally generated levels tailored for particular player personas. Using our game, "Grave Rave", we demonstrate that this approach successfully adapts to four rule-based persona agents over three different experience metrics. Furthermore, the adaptation is shown to be specific in nature, meaning that the levels are persona-conscious, and not just general optimizations with regard to the selected metric.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 7 Dec 2021.

Builds on

9 of 25 references.

References · 9

  1. 2016OpenAI Gymcited by 1,684
  2. 2019Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutionscited by 182
  3. 2018Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Networkcited by 76
  4. 2021Learning Controllable Content Generatorscited by 40
  5. 2021Adversarial Reinforcement Learning for Procedural Content Generationcited by 36
  6. 2018Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristicscited by 28
  7. 2021Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Studycited by 25
  8. 2021Generating Diverse and Competitive Play-Styles for Strategy Gamescited by 6
  9. 2021Fast Game Content Adaptation Through Bayesian-based Player Modellingcited by 3

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Cited by year

Citing papers per year: 2022: 2.

2 papers.

  1. 2022Generative Personas That Behave and Experience Like Humanscited by 7
  2. 2022Generating Game Levels of Diverse Behaviour Engagementcited by 2