Mixed-initiative Procedural Content Generation (PCG) refers to tools or systems in which a human designer works with an algorithm to produce game content.
Personalized therapy, in which a therapeutic practice is adapted to an individual patient, leads to better health outcomes.
Architecture search optimizes the structure of a neural network for some task instead of relying on manual authoring.
One major barrier to applications of deep Reinforcement Learning (RL) both inside and outside of games is the lack of explainability.
We evaluate this representation on its ability to predict affordances for unseen tiles, and to serve as a PLGML representation for annotated and unannotated games.
Procedural content generation via machine learning (PCGML) is the process of procedurally generating game content using models trained on existing game content.
Co-creative Procedural Content Generation via Machine Learning (PCGML) refers to systems where a PCGML agent and a human work together to produce output content.
We evaluate this approach by approximating its ability to provide human users with the explanations of AI agent's actions and helping them to more efficiently cooperate with the AI agent.
In game development, designing compelling visual assets that convey gameplay-relevant features requires time and experience.
Tabletop roleplaying games (TTRPGs) and procedural content generators can both be understood as systems of rules for producing content.
Procedural Level Generation via Machine Learning (PLGML), the study of generating game levels with machine learning, has received a large amount of recent academic attention.
Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning.
One of the defining characteristics of human creativity is the ability to make conceptual leaps, creating something surprising from typical knowledge.
This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content.
We evaluate the extent to which the models represent stylistic level design knowledge and demonstrate the ability of our system to explain levels that were blended by human expert designers.