109 citations · 277 across the 50 of their papers we have counts for
19 papers · 1 filter
Personalizing Exposure Therapy via Reinforcement Learning
Athar Mahmoudi-Nejad, Matthew Guzdial, Pierre Boulanger
Personalized therapy, in which a therapeutic practice is adapted to an individual patient, can lead to improved health outcomes. Typically, this is accomplished by relying on a the…
Stress Detection from Photoplethysmography in a Virtual Reality Environment
Athar Mahmoudi-Nejad, Pierre Boulanger, Matthew Guzdial
Personalized virtual reality exposure therapy is a therapeutic practice that can adapt to an individual patient, leading to better health outcomes. Measuring a patient's mental sta…
Spiders Based on Anxiety: How Reinforcement Learning Can Deliver Desired User Experience in Virtual Reality Personalized Arachnophobia Treatment
Athar Mahmoudi-Nejad, Matthew Guzdial, Pierre Boulanger
The need to generate a spider to provoke a desired anxiety response arises in the context of personalized virtual reality exposure therapy (VRET), a treatment approach for arachnop…
Game Level Blending using a Learned Level Representation
Venkata Sai Revanth Atmakuri, Seth Cooper, Matthew Guzdial
Game level blending via machine learning, the process of combining features of game levels to create unique and novel game levels using Procedural Content Generation via Machine Le…
Transfer Learning for Underrepresented Music Generation
Anahita Doosti, Matthew Guzdial
This paper investigates a combinational creativity approach to transfer learning to improve the performance of deep neural network-based models for music generation on out-of-distr…
Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AI
Emily Halina, Matthew Guzdial
In fighting games, individual players of the same skill level often exhibit distinct strategies from one another through their gameplay. Despite this, the majority of AI agents for…
Clustering-based Tile Embedding (CTE): A General Representation for Level Design with Skewed Tile Distributions
Mrunal Jadhav, Matthew Guzdial
There has been significant research interest in Procedural Level Generation via Machine Learning (PLGML), applying ML techniques to automated level generation. One recent trend is…
Responsibility: An Example-based Explainable AI approach via Training Process Inspection
Faraz Khadivpour, Arghasree Banerjee, Matthew Guzdial
Explainable Artificial Intelligence (XAI) methods are intended to help human users better understand the decision making of an AI agent. However, many modern XAI approaches are uni…
Threshold Designer Adaptation: Improved Adaptation for Designers in Co-creative Systems
Emily Halina, Matthew Guzdial
To best assist human designers with different styles, Machine Learning (ML) systems need to be able to adapt to them. However, there has been relatively little prior work on how an…
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…
Combinets: Creativity via Recombination of Neural Networks
Matthew Guzdial, Mark O. Riedl
One of the defining characteristics of human creativity is the ability to make conceptual leaps, creating something surprising from typical knowledge. In comparison, deep neural ne…