122 citations · 257 across the 38 of their papers we have counts for
21 papers · 1 filter
State Trace Rationale As Auxiliary Task in Reinforcement Learning
Muhammad U. Nasir, Alex Vogt, Steven D. James +1
We propose STRAT, an auxiliary task that trains deep reinforcement learning (RL) agents to predict a short textual trace of their own state. Inspired by human spatial navigation, t…
Procedural Content Metageneration via Program Search and Continual Abstraction Discovery
Matthew Siper, Ahmed Khalifa, Julian Togelius
Large language models can generate executable programs, which makes it possible to search directly over procedural content generators rather than individual levels. We study this a…
Mortar: Evolving Mechanics for Automatic Game Design
Muhammad U. Nasir, Yuchen Li, Steven James +1
We present Mortar, a system for autonomously evolving game mechanics for automatic game design. Game mechanics define the rules and interactions that govern gameplay, and designing…
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…
PuzzleJAX: A Benchmark for Reasoning and Learning
Sam Earle, Graham Todd, Yuchen Li +5
We introduce PuzzleJAX, a GPU-accelerated puzzle game engine and description language designed to support rapid benchmarking of tree search, reinforcement learning, and LLM reasoni…
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…
GVGAI-LLM: Evaluating Large Language Model Agents with Infinite Games
Yuchen Li, Cong Lin, Muhammad Umair Nasir +3
We introduce GVGAI-LLM, a video game benchmark for evaluating the reasoning and problem-solving capabilities of large language models (LLMs). Built on the General Video Game AI fra…
ScriptDoctor: Automatic Generation of PuzzleScript Games via Large Language Models and Tree Search
Sam Earle, Ahmed Khalifa, Muhammad Umair Nasir +4
There is much interest in using large pre-trained models in Automatic Game Design (AGD), whether via the generation of code, assets, or more abstract conceptualization of design id…
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…
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…
GAVEL: Generating Games Via Evolution and Language Models
Graham Todd, Alexander Padula, Matthew Stephenson +3
Automatically generating novel and interesting games is a complex task. Challenges include representing game rules in a computationally workable form, searching through the large s…
Goals as Reward-Producing Programs
Guy Davidson, Graham Todd, Julian Togelius +2
People are remarkably capable of generating their own goals, beginning with child's play and continuing into adulthood. Despite considerable empirical and computational work on goa…
The Ink Splotch Effect: A Case Study on ChatGPT as a Co-Creative Game Designer
Asad Anjum, Yuting Li, Noelle Law +2
This paper studies how large language models (LLMs) can act as effective, high-level creative collaborators and ``muses'' for game design. We model the design of this study after t…
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…
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…
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…