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Found 12 papers, 0 papers with code
Procedural Content Generation via Knowledge Transformation (PCG-KT)
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 content generation is enabled by the process of knowledge transformation -- transforming knowledge derived from one domain in order to apply it in another.
tile2tile: Learning Game Filters for Platformer Style Transfer
We present tile2tile, an approach for style transfer between levels of tile-based platformer games.
Latent Combinational Game Design
This enables generating new games that blend the input games as well as controlling the relative proportions of each game in the blend.
Procedural Content Generation using Behavior Trees (PCGBT)
Behavior trees (BTs) are a popular method for modeling NPC and enemy AI behavior and have been widely used in commercial games.
Dungeon and Platformer Level Blending and Generation using Conditional VAEs
Variational autoencoders (VAEs) have been used in prior works for generating and blending levels from different games.
Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder
We test our method using models for 5 different platformer games as well as a blended domain spanning 3 of these games.
Conditional Level Generation and Game Blending
Prior research has shown variational autoencoders (VAEs) to be useful for generating and blending game levels by learning latent representations of existing level data.
Exploring Level Blending across Platformers via Paths and Affordances
Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content.
Towards Game Design via Creative Machine Learning (GDCML)
In recent years, machine learning (ML) systems have been increasingly applied for performing creative tasks.
Sequential Segment-based Level Generation and Blending using Variational Autoencoders
In this paper, we build on these methods by training VAEs to learn a sequential model of segment generation such that generated segments logically follow from prior segments.
Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders
In this paper, we present a PCGML approach for level generation that is able to recombine, adapt, and reuse structural patterns from several domains to approximate unseen domains.
Controllable Level Blending between Games using Variational Autoencoders
We then use this space to generate level segments that combine properties of levels from both games.
