83 citations · 227 across the 21 of their papers we have counts for
29 papers
Evolutionary Machine Learning and Games
Julian Togelius, Ahmed Khalifa, Sam Earle +2
Evolutionary machine learning (EML) has been applied to games in multiple ways, and for multiple different purposes. Importantly, AI research in games is not only about playing gam…
Level Generation Through Large Language Models
Graham Todd, Sam Earle, Muhammad Umair Nasir +2
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 genera…
Learning Controllable 3D Level Generators
Zehua Jiang, Sam Earle, Michael Cerny Green +1
Procedural Content Generation via Reinforcement Learning (PCGRL) foregoes the need for large human-authored data-sets and allows agents to train explicitly on functional constraint…
Mutation Models: Learning to Generate Levels by Imitating Evolution
Ahmed Khalifa, Michael Cerny Green, Julian Togelius
Search-based procedural content generation (PCG) is a well-known method for level generation in games. Its key advantage is that it is generic and able to satisfy functional constr…
Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials
Michael Cerny Green, Ahmed Khalifa, M Charity +1
In this paper, we present a method for automated persona-driven video game tutorial level generation. Tutorial levels are scenarios in which the player can explore and discover dif…
Predicting Personas Using Mechanic Frequencies and Game State Traces
Michael Cerny Green, Ahmed Khalifa, M Charity +2
We investigate how to efficiently predict play personas based on playtraces. Play personas can be computed by calculating the action agreement ratio between a player and a generati…