22 citations · 51 across the 13 of their papers we have counts for
14 papers
Generative Modeling of Individual Behavior at Scale
Nabil Omi, Lucas Caccia, Anurag Sarkar +2
There has been a growing interest in using AI to model human behavior, particularly in domains where humans interact with this technology. While most existing work models human beh…
Procedural Content Generation via Knowledge Transformation (PCG-KT)
Anurag Sarkar, Matthew Guzdial, Sam Snodgrass +3
We introduce the concept of Procedural Content Generation via Knowledge Transformation (PCG-KT), a new lens and framework for characterizing PCG methods and approaches in which con…
tile2tile: Learning Game Filters for Platformer Style Transfer
Anurag Sarkar, Seth Cooper
We present tile2tile, an approach for style transfer between levels of tile-based platformer games. Our method involves training models that translate levels from a lower-resolutio…
Latent Combinational Game Design
Anurag Sarkar, Seth Cooper
We present latent combinational game design -- an approach for generating playable games that blend a given set of games in a desired combination using deep generative latent varia…
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…
Towards Game Design via Creative Machine Learning (GDCML)
Anurag Sarkar, Seth Cooper
In recent years, machine learning (ML) systems have been increasingly applied for performing creative tasks. Such creative ML approaches have seen wide use in the domains of visual…
Sequential Segment-based Level Generation and Blending using Variational Autoencoders
Anurag Sarkar, Seth Cooper
Existing methods of level generation using latent variable models such as VAEs and GANs do so in segments and produce the final level by stitching these separately generated segmen…
Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders
Sam Snodgrass, Anurag Sarkar
Procedural content generation via machine learning (PCGML) has demonstrated its usefulness as a content and game creation approach, and has been shown to be able to support human c…
Controllable Level Blending between Games using Variational Autoencoders
Anurag Sarkar, Zhihan Yang, Seth Cooper
Previous work explored blending levels from existing games to create levels for a new game that mixes properties of the original games. In this paper, we use Variational Autoencode…