22 citations · 40 across the 7 of their papers we have counts for
10 papers
Procedural Content Generation using Behavior Trees (PCGBT)
Anurag Sarkar, Seth Cooper
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 play…
Dungeon and Platformer Level Blending and Generation using Conditional VAEs
Anurag Sarkar, Seth Cooper
Variational autoencoders (VAEs) have been used in prior works for generating and blending levels from different games. To add controllability to these models, conditional VAEs (CVA…
Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder
Anurag Sarkar, Seth Cooper
Several works have demonstrated the use of variational autoencoders (VAEs) for generating levels in the style of existing games and blending levels across different games. Further,…
Conditional Level Generation and Game Blending
Anurag Sarkar, Zhihan Yang, Seth Cooper
Prior research has shown variational autoencoders (VAEs) to be useful for generating and blending game levels by learning latent representations of existing level data. We build on…
Game Level Clustering and Generation using Gaussian Mixture VAEs
Zhihan Yang, Anurag Sarkar, Seth Cooper
Variational autoencoders (VAEs) have been shown to be able to generate game levels but require manual exploration of the learned latent space to generate outputs with desired attri…
Exploring Level Blending across Platformers via Paths and Affordances
Anurag Sarkar, Adam Summerville, Sam Snodgrass +2
Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content. While used primarily for producing new con…