Procedural Content Generation using Behavior Trees (PCGBT)
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Abstract (arXiv)
Behavior trees (BTs) are a popular method for modeling NPC and enemy AI behavior and have been widely used in commercial games. In this work, rather than use BTs to model game playing agents, we use them for modeling game design agents, defining behaviors as content generation tasks rather than in-game actions. Similar to how traditional BTs enable modeling behaviors in a modular and dynamic manner, BTs for PCG enable simple subtrees for generating parts of levels to be combined modularly to form complex trees for generating whole levels as well as generators that can dynamically vary the generated content. We refer to this approach as Procedural Content Generation using Behavior Trees, or PCGBT, and demonstrate it by using BTs to model generators for Super Mario Bros., Mega Man and Metroid levels as well as dungeon layouts and discuss several ways in which this paradigm could be applied and extended in the future.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 32 references that are in the map. First posted on arXiv 24 Jun 2021.
Builds on
7 of 32 references.
References · 7
- 2017Procedural Content Generation via Machine Learning (PCGML)cited by 137
- 2017Behavior Trees in Robotics and AI: An Introductioncited by 107
- 2020PCGRL: Procedural Content Generation via Reinforcement Learningcited by 90
- 2020A Survey of Behavior Trees in Robotics and AIcited by 55
- 2016The VGLC: The Video Game Level Corpuscited by 41
- 2020"It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Gamescited by 17
- 2018Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learningcited by 1
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Citing papers per year: 2022: 2.
2 papers.
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- 2022On Linking Level Segmentscited by 1
- 2022Aesthetic Bot: Interactively Evolving Game Maps on Twitter9 Aug 2022
- 2022On Linking Level Segments9 Mar 2022