122 citations · 260 across the 25 of their papers we have counts for
26 papers
A Markovian Framing of WaveFunctionCollapse for Procedurally Generating Aesthetically Complex Environments
Franklin Yiu, Mohan Lu, Nina Li +5
Procedural content generation often requires satisfying both designer-specified objectives and adjacency constraints implicitly imposed by the underlying tile set. To address the c…
Video Game Level Design as a Multi-Agent Reinforcement Learning Problem
Sam Earle, Zehua Jiang, Eugene Vinitsky +1
Procedural Content Generation via Reinforcement Learning (PCGRL) offers a method for training controllable level designer agents without the need for human datasets, using metrics…
All Stories Are One Story: Emotional Arc Guided Procedural Game Level Generation
Yunge Wen, Chenliang Huang, Hangyu Zhou +5
The emotional arc is a universal narrative structure underlying stories across cultures and media -- an idea central to structuralist narratology, often encapsulated in the phrase…
Enhancing Player Enjoyment with a Two-Tier DRL and LLM-Based Agent System for Fighting Games
Shouren Wang, Zehua Jiang, Fernando Sliva +2
Deep reinforcement learning (DRL) has effectively enhanced gameplay experiences and game design across various game genres. However, few studies on fighting game agents have focuse…
Amorphous Fortress Online: Collaboratively Designing Open-Ended Multi-Agent AI and Game Environments
M Charity, Mayu Wilson, Steven Lee +3
This work introduces Amorphous Fortress Online -- a web-based platform where users can design petri-dish-like environments and games consisting of multi-agent AI characters. Users…
PCGRLLM: Large Language Model-Driven Reward Design for Procedural Content Generation Reinforcement Learning
In-Chang Baek, Sung-Hyun Kim, Sam Earle +4
Reward design plays a pivotal role in the training of game AIs, requiring substantial domain-specific knowledge and human effort. In recent years, several studies have explored rew…
PCGRL+: Scaling, Control and Generalization in Reinforcement Learning Level Generators
Sam Earle, Zehua Jiang, Julian Togelius
Procedural Content Generation via Reinforcement Learning (PCGRL) has been introduced as a means by which controllable designer agents can be trained based only on a set of computab…
Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents
Sam Earle, Julian Togelius
We introduce Autoverse, an evolvable, domain-specific language for single-player 2D grid-based games, and demonstrate its use as a scalable training ground for Open-Ended Learning…
Making New Connections: LLMs as Puzzle Generators for The New York Times' Connections Word Game
Tim Merino, Sam Earle, Ryan Sudhakaran +2
The Connections puzzle is a word association game published daily by The New York Times (NYT). In this game, players are asked to find groups of four words that are connected by a…
DreamCraft: Text-Guided Generation of Functional 3D Environments in Minecraft
Sam Earle, Filippos Kokkinos, Yuhe Nie +2
Procedural Content Generation (PCG) algorithms enable the automatic generation of complex and diverse artifacts. However, they don't provide high-level control over the generated c…
Missed Connections: Lateral Thinking Puzzles for Large Language Models
Graham Todd, Tim Merino, Sam Earle +1
The Connections puzzle published each day by the New York Times tasks players with dividing a bank of sixteen words into four groups of four words that each relate to a common them…
Large Language Models and Games: A Survey and Roadmap
Roberto Gallotta, Graham Todd, Marvin Zammit +4
Recent years have seen an explosive increase in research on large language models (LLMs), and accompanying public engagement on the topic. While starting as a niche area within nat…
Quality Diversity in the Amorphous Fortress (QD-AF): Evolving for Complexity in 0-Player Games
Sam Earle, M Charity, Dipika Rajesh +2
We explore the generation of diverse environments using the Amorphous Fortress (AF) simulation framework. AF defines a set of Finite State Machine (FSM) nodes and edges that can be…
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…
Amorphous Fortress: Observing Emergent Behavior in Multi-Agent FSMs
M Charity, Dipika Rajesh, Sam Earle +1
We introduce a system called Amorphous Fortress -- an abstract, yet spatial, open-ended artificial life simulation. In this environment, the agents are represented as finite-state…
LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization
Muhammad U. Nasir, Sam Earle, Christopher Cleghorn +2
Large Language Models (LLMs) have emerged as powerful tools capable of accomplishing a broad spectrum of tasks. Their abilities span numerous areas, and one area where they have ma…
Controllable Path of Destruction
Matthew Siper, Sam Earle, Zehua Jiang +2
Path of Destruction (PoD) is a self-supervised method for learning iterative generators. The core idea is to produce a training set by destroying a set of artifacts, and for each d…
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…
Pathfinding Neural Cellular Automata
Sam Earle, Ozlem Yildiz, Julian Togelius +1
Pathfinding makes up an important sub-component of a broad range of complex tasks in AI, such as robot path planning, transport routing, and game playing. While classical algorithm…
Generating Diverse Indoor Furniture Arrangements
Ya-Chuan Hsu, Matthew C. Fontaine, Sam Earle +3
We present a method for generating arrangements of indoor furniture from human-designed furniture layout data. Our method creates arrangements that target specified diversity, such…
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…
Illuminating Diverse Neural Cellular Automata for Level Generation
Sam Earle, Justin Snider, Matthew C. Fontaine +2
We present a method of generating diverse collections of neural cellular automata (NCA) to design video game levels. While NCAs have so far only been trained via supervised learnin…
Exploring open-ended gameplay features with Micro RollerCoaster Tycoon
Michael Cerny Green, Victoria Yen, Sam Earle +3
This paper introduces MicroRCT, a novel open source simulator inspired by the theme park sandbox game RollerCoaster Tycoon. The goal in MicroRCT is to place rides and shops in an a…
Learning Controllable Content Generators
Sam Earle, Maria Edwards, Ahmed Khalifa +2
It has recently been shown that reinforcement learning can be used to train generators capable of producing high-quality game levels, with quality defined in terms of some user-spe…