2021/06/24 by Anurag Sarkar, Seth Cooper, Sarkar, Anurag +1
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Digital Games and Media #FOS: Computer and information sciences #Reinforcement Learning in Robotics #cs.AI
paper · pdf · doi:10.48550/arxiv.2107.06638
Accepted to EXAG 2021
openalex publication_date 2021/06/24 · arxiv created 2021/10/08 · arxiv updated 2021/10/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.