122 citations · 257 across the 38 of their papers we have counts for
41 papers
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…
Programmable Cellular Automata
Ahmed Khalifa, Muhammad Umair Nasir, Matthew Siper +2
Cellular automata is a local computation paradigm where complex behavior can arise from local interactions between simple functions. This paradigm has been used to explain many sys…
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…
The Time Value of Evolution
Matthew Siper, Ahmed Khalifa, Julian Togelius
In evolutionary search, a weak child can be a valuable ancestor that makes high-fitness regions reachable. Immediate-return control is blind to this delayed utility, penalizing mut…
ELMER: Evolutionary Language Model that Explores and Refines
Matthew Siper, Ahmed Khalifa, Julian Togelius
Program evolution can measure whether a mutation helped, but it rarely controls how far the mutation moves in behavior space. Syntactic edit size is an unreliable proxy: a small co…
Evolutionary Wave Function Collapse
Dipika Rajesh, Ahmed Khalifa, Julian Togelius
Wave Function Collapse (WFC) is a widely used procedural content generation method that learns local adjacency constraints from example inputs to generate larger outputs. In this p…
Continuous Program Search
Matthew Siper, Muhammad Umair Nasir, Ahmed Khalifa +3
Genetic Programming yields interpretable programs, but small syntactic mutations can induce large, unpredictable behavioral shifts, degrading locality and sample efficiency. We fra…
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…
Word2Minecraft: Generating 3D Game Levels through Large Language Models
Shuo Huang, Muhammad Umair Nasir, Steven James +1
We present Word2Minecraft, a system that leverages large language models to generate playable game levels in Minecraft based on structured stories. The system transforms narrative…
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…
GameTraversalBenchmark: Evaluating Planning Abilities Of Large Language Models Through Traversing 2D Game Maps
Muhammad Umair Nasir, Steven James, Julian Togelius
Large language models (LLMs) have recently demonstrated great success in generating and understanding natural language. While they have also shown potential beyond the domain of na…
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…
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…
Word2World: Generating Stories and Worlds through Large Language Models
Muhammad U. Nasir, Steven James, Julian Togelius
Large Language Models (LLMs) have proven their worth across a diverse spectrum of disciplines. LLMs have shown great potential in Procedural Content Generation (PCG) as well, but d…