125 research outputs found
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. While used
primarily for producing new content in the style of the game domain used for
training, recent works have increasingly started to explore methods for
discovering and generating content in novel domains via techniques such as
level blending and domain transfer. In this paper, we build on these works and
introduce a new PCGML approach for producing novel game content spanning
multiple domains. We use a new affordance and path vocabulary to encode data
from six different platformer games and train variational autoencoders on this
data, enabling us to capture the latent level space spanning all the domains
and generate new content with varying proportions of the different domains.Comment: 6 pages, 5 figures, 16th AAAI Conference on Artificial Intelligence
and Interactive Digital Entertainment (AIIDE 2020
Level Generation Through Large Language Models
Large Language Models (LLMs) are powerful tools, capable of leveraging their
training on natural language to write stories, generate code, and answer
questions. But can they generate functional video game levels? Game levels,
with their complex functional constraints and spatial relationships in more
than one dimension, are very different from the kinds of data an LLM typically
sees during training. Datasets of game levels are also hard to come by,
potentially taxing the abilities of these data-hungry models. We investigate
the use of LLMs to generate levels for the game Sokoban, finding that LLMs are
indeed capable of doing so, and that their performance scales dramatically with
dataset size. We also perform preliminary experiments on controlling LLM level
generators and discuss promising areas for future work
Deep learning for procedural content generation
Summarization: Procedural content generation in video games has a long history. Existing procedural content generation methods, such as search-based, solver-based, rule-based and grammar-based methods have been applied to various content types such as levels, maps, character models, and textures. A research field centered on content generation in games has existed for more than a decade. More recently, deep learning has powered a remarkable range of inventions in content production, which are applicable to games. While some cutting-edge deep learning methods are applied on their own, others are applied in combination with more traditional methods, or in an interactive setting. This article surveys the various deep learning methods that have been applied to generate game content directly or indirectly, discusses deep learning methods that could be used for content generation purposes but are rarely used today, and envisages some limitations and potential future directions of deep learning for procedural content generation.Presented on: Neural Computing and Application
Latent Combinational Game Design
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 variable models. We use Gaussian Mixture Variational
Autoencoders (GMVAEs) which model the VAE latent space via a mixture of
Gaussian components. Through supervised training, each component encodes levels
from one game and lets us define blended games as linear combinations of these
components. This enables generating new games that blend the input games and
controlling the relative proportions of each game in the blend. We also extend
prior blending work using conditional VAEs and compare against the GMVAE and
additionally introduce a hybrid conditional GMVAE (CGMVAE) architecture which
lets us generate whole blended levels and layouts. Results show that the above
approaches can generate playable games that blend the input games in specified
combinations. We use both platformers and dungeon-based games to demonstrate
our results
- …