Search Results for author: Amy K. Hoover

Found 9 papers, 6 papers with code

Watts: Infrastructure for Open-Ended Learning

1 code implementation28 Apr 2022 Aaron Dharna, Charlie Summers, Rohin Dasari, Julian Togelius, Amy K. Hoover

This paper proposes a framework called Watts for implementing, comparing, and recombining open-ended learning (OEL) algorithms.

Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding

1 code implementation7 Dec 2021 Yulun Zhang, Matthew C. Fontaine, Amy K. Hoover, Stefanos Nikolaidis

In a Hearthstone deckbuilding case study, we show that our approach improves the sample efficiency of MAP-Elites and outperforms a model trained offline with random decks, as well as a linear surrogate model baseline, setting a new state-of-the-art for quality diversity approaches in automated Hearthstone deckbuilding.

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

4 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.

The Many AI Challenges of Hearthstone

no code implementations15 Jul 2019 Amy K. Hoover, Julian Togelius, Scott Lee, Fernando De Mesentier Silva

Games have benchmarked AI methods since the inception of the field, with classic board games such as Chess and Go recently leaving room for video games with related yet different sets of challenges.

Board Games Card Games

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.

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

Cannot find the paper you are looking for? You can Submit a new open access paper.