109 citations · 277 across the 50 of their papers we have counts for
11 papers · 1 filter
The Definition-Context-Purpose Paradigm and Other Insights from Industry Professionals About the Definition of a Quest
Kristen K. Yu, Matthew Guzdial, Nathan R. Sturtevant
Among academic communities there is no single agreed upon definition of a quest. The industry perspective on this topic is also largely unknown. Thus, thee purpose of this paper is…
The Impact of Visualizing Design Gradients for Human Designers
Matthew Guzdial, Nathan Sturtevant, Carolyn Yang
Mixed-initiative Procedural Content Generation (PCG) refers to tools or systems in which a human designer works with an algorithm to produce game content. This area of research rem…
Arachnophobia Exposure Therapy using Experience-driven Procedural Content Generation via Reinforcement Learning (EDPCGRL)
Athar Mahmoudi-Nejad, Matthew Guzdial, Pierre Boulanger
Personalized therapy, in which a therapeutic practice is adapted to an individual patient, leads to better health outcomes. Typically, this is accomplished by relying on a therapis…
Explaining Deep Reinforcement Learning Agents In The Atari Domain through a Surrogate Model
Alexander Sieusahai, Matthew Guzdial
One major barrier to applications of deep Reinforcement Learning (RL) both inside and outside of games is the lack of explainability. In this paper, we describe a lightweight and e…
Tile Embedding: A General Representation for Procedural Level Generation via Machine Learning
Mrunal Jadhav, Matthew Guzdial
In recent years, Procedural Level Generation via Machine Learning (PLGML) techniques have been applied to generate game levels with machine learning. These approaches rely on human…
Conceptual Expansion Neural Architecture Search (CENAS)
Mohan Singamsetti, Anmol Mahajan, Matthew Guzdial
Architecture search optimizes the structure of a neural network for some task instead of relying on manual authoring. However, it is slow, as each potential architecture is typical…
Toward Co-creative Dungeon Generation via Transfer Learning
Zisen Zhou, Matthew Guzdial
Co-creative Procedural Content Generation via Machine Learning (PCGML) refers to systems where a PCGML agent and a human work together to produce output content. One of the limitat…
Generating Lode Runner Levels by Learning Player Paths with LSTMs
Kynan Sorochan, Jerry Chen, Yakun Yu +1
Machine learning has been a popular tool in many different fields, including procedural content generation. However, procedural content generation via machine learning (PCGML) appr…
Ensemble Learning For Mega Man Level Generation
Bowei Li, Ruohan Chen, Yuqing Xue +3
Procedural content generation via machine learning (PCGML) is the process of procedurally generating game content using models trained on existing game content. PCGML methods can s…
TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games
Emily Halina, Matthew Guzdial
Generating rhythm game charts from songs via machine learning has been a problem of increasing interest in recent years. However, all existing systems struggle to replicate human-l…
Adversarial Random Forest Classifier for Automated Game Design
Thomas Maurer, Matthew Guzdial
Autonomous game design, generating games algorithmically, has been a longtime goal within the technical games research field. However, existing autonomous game design systems have…