Search Results for author: Matthew Guzdial

Found 29 papers, 2 papers with code

Pixel VQ-VAEs for Improved Pixel Art Representation

no code implementations23 Mar 2022 Akash Saravanan, Matthew Guzdial

Machine learning has had a great deal of success in image processing.

The Impact of Visualizing Design Gradients for Human Designers

no code implementations7 Oct 2021 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.

Conceptual Expansion Neural Architecture Search (CENAS)

no code implementations7 Oct 2021 Mohan Singamsetti, Anmol Mahajan, Matthew Guzdial

Architecture search optimizes the structure of a neural network for some task instead of relying on manual authoring.

Neural Architecture Search Transfer Learning

Explaining Deep Reinforcement Learning Agents In The Atari Domain through a Surrogate Model

no code implementations7 Oct 2021 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.

Atari Games Decision Making +1

Tile Embedding: A General Representation for Procedural Level Generation via Machine Learning

no code implementations7 Oct 2021 Mrunal Jadhav, Matthew Guzdial

We evaluate this representation on its ability to predict affordances for unseen tiles, and to serve as a PLGML representation for annotated and unannotated games.

Ensemble Learning For Mega Man Level Generation

no code implementations27 Jul 2021 Bowei Li, Ruohan Chen, Yuqing Xue, Ricky Wang, Wenwen Li, Matthew Guzdial

Procedural content generation via machine learning (PCGML) is the process of procedurally generating game content using models trained on existing game content.

Ensemble Learning

Toward Co-creative Dungeon Generation via Transfer Learning

no code implementations27 Jul 2021 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.

Transfer Learning

Generating Lode Runner Levels by Learning Player Paths with LSTMs

no code implementations27 Jul 2021 Kynan Sorochan, Jerry Chen, Yakun Yu, Matthew Guzdial

Machine learning has been a popular tool in many different fields, including procedural content generation.

TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games

no code implementations26 Jul 2021 Emily Halina, Matthew Guzdial

Generating rhythm game charts from songs via machine learning has been a problem of increasing interest in recent years.

Adversarial Random Forest Classifier for Automated Game Design

no code implementations26 Jul 2021 Thomas Maurer, Matthew Guzdial

Autonomous game design, generating games algorithmically, has been a longtime goal within the technical games research field.

Explainability via Responsibility

no code implementations4 Oct 2020 Faraz Khadivpour, Matthew Guzdial

We evaluate this approach by approximating its ability to provide human users with the explanations of AI agent's actions and helping them to more efficiently cooperate with the AI agent.

Explainable artificial intelligence

Generating Gameplay-Relevant Art Assets with Transfer Learning

1 code implementation4 Oct 2020 Adrian Gonzalez, Matthew Guzdial, Felix Ramos

In game development, designing compelling visual assets that convey gameplay-relevant features requires time and experience.

Image Generation Transfer Learning

Entity Embedding as Game Representation

no code implementations4 Oct 2020 Nazanin Yousefzadeh Khameneh, Matthew Guzdial

However, the majority of the work has focused on the production of static game content, including game levels and visual elements.

Entity Embeddings

Tabletop Roleplaying Games as Procedural Content Generators

no code implementations12 Jul 2020 Matthew Guzdial, Devi Acharya, Max Kreminski, Michael Cook, Mirjam Eladhari, Antonios Liapis, Anne Sullivan

Tabletop roleplaying games (TTRPGs) and procedural content generators can both be understood as systems of rules for producing content.

Conceptual Game Expansion

no code implementations22 Feb 2020 Matthew Guzdial, Mark Riedl

Automated game design is the problem of automatically producing games through computational processes.

Integrating Automated Play in Level Co-Creation

no code implementations20 Nov 2019 Andrew Hoyt, Matthew Guzdial, Yalini Kumar, Gillian Smith, Mark O. Riedl

In level co-creation an AI and human work together to create a video game level.

Automated Let's Play Commentary

no code implementations5 Sep 2019 Shukan Shah, Matthew Guzdial, Mark O. Riedl

Let's Plays of video games represent a relatively unexplored area for experimental AI in games.

Making CNNs for Video Parsing Accessible

no code implementations10 Jun 2019 Zijin Luo, Matthew Guzdial, Mark Riedl

This serves as a barrier to groups who don't possess this access.

An Interaction Framework for Studying Co-Creative AI

no code implementations22 Mar 2019 Matthew Guzdial, Mark Riedl

Machine learning has been applied to a number of creative, design-oriented tasks.

Co-Creative Level Design via Machine Learning

no code implementations25 Sep 2018 Matthew Guzdial, Nicholas Liao, Mark Riedl

Procedural Level Generation via Machine Learning (PLGML), the study of generating game levels with machine learning, has received a large amount of recent academic attention.

Explainable PCGML via Game Design Patterns

no code implementations25 Sep 2018 Matthew Guzdial, Joshua Reno, Jonathan Chen, Gillian Smith, Mark Riedl

Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning.

Towards Automated Let's Play Commentary

no code implementations25 Sep 2018 Matthew Guzdial, Shukan Shah, Mark Riedl

We introduce the problem of generating Let's Play-style commentary of gameplay video via machine learning.

Automated Game Design via Conceptual Expansion

no code implementations6 Sep 2018 Matthew Guzdial, Mark Riedl

To the best of our knowledge, this represents the first machine learning-based automated game design system.

Creative Invention Benchmark

1 code implementation9 May 2018 Matthew Guzdial, Nicholas Liao, Vishwa Shah, Mark O. Riedl

In this paper we present the Creative Invention Benchmark (CrIB), a 2000-problem benchmark for evaluating a particular facet of computational creativity.

Combinets: Creativity via Recombination of Neural Networks

no code implementations10 Feb 2018 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.

General Classification Image Classification +2

Procedural Content Generation via Machine Learning (PCGML)

no code implementations2 Feb 2017 Adam Summerville, Sam Snodgrass, Matthew Guzdial, Christoffer Holmgård, Amy K. Hoover, Aaron Isaksen, Andy Nealen, Julian Togelius

This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content.

Card Games Style Transfer

Learning to Blend Computer Game Levels

no code implementations8 Mar 2016 Matthew Guzdial, Mark Riedl

We evaluate the extent to which the models represent stylistic level design knowledge and demonstrate the ability of our system to explain levels that were blended by human expert designers.

Toward Game Level Generation from Gameplay Videos

no code implementations23 Feb 2016 Matthew Guzdial, Mark Riedl

We further demonstrate how the acquired design knowledge can be used to generate sections of game levels.

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