143 citations · 546 across the 35 of their papers we have counts for
26 papers · 1 filter
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…
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…
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…
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…
Generative Personas That Behave and Experience Like Humans
Matthew Barthet, Ahmed Khalifa, Antonios Liapis +1
Using artificial intelligence (AI) to automatically test a game remains a critical challenge for the development of richer and more complex game worlds and for the advancement of A…
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…
Game Mechanic Alignment Theory and Discovery
Michael Cerny Green, Ahmed Khalifa, Philip Bontrager +2
We present a new concept called Game Mechanic Alignment theory as a way to organize game mechanics through the lens of systemic rewards and agential motivations. By disentangling p…
Deep Learning for Procedural Content Generation
Jialin Liu, Sam Snodgrass, Ahmed Khalifa +3
Procedural content generation in video games has a long history. Existing procedural content generation methods, such as search-based, solver-based, rule-based and grammar-based me…
Mixed-Initiative Level Design with RL Brush
Omar Delarosa, Hang Dong, Mindy Ruan +2
This paper introduces RL Brush, a level-editing tool for tile-based games designed for mixed-initiative co-creation. The tool uses reinforcement-learning-based models to augment ma…
Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network
Matthew C. Fontaine, Ruilin Liu, Ahmed Khalifa +4
Generative adversarial networks (GANs) are quickly becoming a ubiquitous approach to procedurally generating video game levels. While GAN generated levels are stylistically similar…
Mario Level Generation From Mechanics Using Scene Stitching
Michael Cerny Green, Luvneesh Mugrai, Ahmed Khalifa +1
This paper presents a level generation method for Super Mario by stitching together pre-generated "scenes" that contain specific mechanics, using mechanic-sequences from agent play…
Mech-Elites: Illuminating the Mechanic Space of GVGAI
M Charity, Michael Cerny Green, Ahmed Khalifa +1
This paper introduces a fully automatic method of mechanic illumination for general video game level generation. Using the Constrained MAP-Elites algorithm and the GVG-AI framework…
Automatic Critical Mechanic Discovery Using Playtraces in Video Games
Michael Cerny Green, Ahmed Khalifa, Gabriella A. B. Barros +2
We present a new method of automatic critical mechanic discovery for video games using a combination of game description parsing and playtrace information. This method is applied t…
General Video Game Rule Generation
Ahmed Khalifa, Michael Cerny Green, Diego Perez-Liebana +1
We introduce the General Video Game Rule Generation problem, and the eponymous software framework which will be used in a new track of the General Video Game AI (GVGAI) competition…
Two-step Constructive Approaches for Dungeon Generation
Michael Cerny Green, Ahmed Khalifa, Athoug Alsoughayer +3
This paper presents a two-step generative approach for creating dungeons in the rogue-like puzzle game MiniDungeons 2. Generation is split into two steps, initially producing the a…
Intentional Computational Level Design
Ahmed Khalifa, Michael Cerny Green, Gabriella Barros +1
The procedural generation of levels and content in video games is a challenging AI problem. Often such generation relies on an intelligent way of evaluating the content being gener…
Tree Search vs Optimization Approaches for Map Generation
Debosmita Bhaumik, Ahmed Khalifa, Michael Cerny Green +1
Search-based procedural content generation uses stochastic global optimization algorithms to search for game content. However, standard tree search algorithms can be competitive wi…
Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning
Arthur Juliani, Ahmed Khalifa, Vincent-Pierre Berges +6
The rapid pace of recent research in AI has been driven in part by the presence of fast and challenging simulation environments. These environments often take the form of games; wi…
A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking
Matthew Stephenson, Damien Anderson, Ahmed Khalifa +4
This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms. This method w…
Generating Levels That Teach Mechanics
Michael Cerny Green, Ahmed Khalifa, Gabriella A. B. Barros +2
The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is pla…
AtDelfi: Automatically Designing Legible, Full Instructions For Games
Michael Cerny Green, Ahmed Khalifa, Gabriella A. B. Barros +3
This paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (AuTomatically DEsigning Legible, Full Instructions for games) was created to…
Talakat: Bullet Hell Generation through Constrained Map-Elites
Ahmed Khalifa, Scott Lee, Andy Nealen +1
We describe a search-based approach to generating new levels for bullet hell games, which are action games characterized by and requiring avoidance of a very large amount of projec…