Search Results for author: Matthew C. Fontaine

Found 8 papers, 7 papers with code

Illuminating Diverse Neural Cellular Automata for Level Generation

2 code implementations12 Sep 2021 Sam Earle, Justin Snider, Matthew C. Fontaine, Stefanos Nikolaidis, Julian Togelius

We present a method of generating a collection of neural cellular automata (NCA) to design video game levels.

On the Importance of Environments in Human-Robot Coordination

3 code implementations21 Jun 2021 Matthew C. Fontaine, Ya-Chuan Hsu, Yulun Zhang, Bryon Tjanaka, Stefanos Nikolaidis

When studying robots collaborating with humans, much of the focus has been on robot policies that coordinate fluently with human teammates in collaborative tasks.

Differentiable Quality Diversity

1 code implementation NeurIPS 2021 Matthew C. Fontaine, Stefanos Nikolaidis

Quality diversity (QD) is a growing branch of stochastic optimization research that studies the problem of generating an archive of solutions that maximize a given objective function but are also diverse with respect to a set of specified measure functions.

Stochastic Optimization

Video Game Level Repair via Mixed Integer Linear Programming

1 code implementation13 Oct 2020 Hejia Zhang, Matthew C. Fontaine, Amy K. Hoover, Julian Togelius, Bistra Dilkina, Stefanos Nikolaidis

Recent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples.

Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network

1 code implementation11 Jul 2020 Matthew C. Fontaine, Ruilin Liu, Ahmed Khalifa, Jignesh Modi, Julian Togelius, Amy K. Hoover, Stefanos Nikolaidis

Generative adversarial networks (GANs) are quickly becoming a ubiquitous approach to procedurally generating video game levels.

Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space

3 code implementations5 Dec 2019 Matthew C. Fontaine, Julian Togelius, Stefanos Nikolaidis, Amy K. Hoover

Results from experiments based on standard continuous optimization benchmarks show that CMA-ME finds better-quality solutions than MAP-Elites; similarly, results on the strategic game Hearthstone show that CMA-ME finds both a higher overall quality and broader diversity of strategies than both CMA-ES and MAP-Elites.

Evolving the Hearthstone Meta

no code implementations2 Jul 2019 Fernando de Mesentier Silva, Rodrigo Canaan, Scott Lee, Matthew C. Fontaine, Julian Togelius, Amy K. Hoover

Balancing an ever growing strategic game of high complexity, such as Hearthstone is a complex task.

Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries

1 code implementation24 Apr 2019 Matthew C. Fontaine, Scott Lee, L. B. Soros, Fernando De Mesentier Silva, Julian Togelius, Amy K. Hoover

Quality diversity (QD) algorithms such as MAP-Elites have emerged as a powerful alternative to traditional single-objective optimization methods.

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