no code implementations • 1 Dec 2023 • Ehsan Beikihassan, Amy K. Hoover, Ioannis Koutis, Ali Parviz, Niloofar Aghaieabiane
We consider a setting where a population of artificial learners is given, and the objective is to optimize aggregate measures of performance, under constraints on training resources.
1 code implementation • 23 Feb 2023 • Elliot Meyerson, Mark J. Nelson, Herbie Bradley, Adam Gaier, Arash Moradi, Amy K. Hoover, Joel Lehman
The promise of such language model crossover (which is simple to implement and can leverage many different open-source language models) is that it enables a simple mechanism to evolve semantically-rich text representations (with few domain-specific tweaks), and naturally benefits from current progress in language models.
1 code implementation • 28 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.
1 code implementation • 7 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.
1 code implementation • 13 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.
1 code implementation • 11 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.
6 code implementations • 5 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.
no code implementations • 15 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.
no code implementations • 2 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.
1 code implementation • 24 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.
no code implementations • 2 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.