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Found 11 papers, 7 papers with code
Procedural Content Generation via Machine Learning (PCGML)
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.
Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries
Quality diversity (QD) algorithms such as MAP-Elites have emerged as a powerful alternative to traditional single-objective optimization methods.
Evolving the Hearthstone Meta
Balancing an ever growing strategic game of high complexity, such as Hearthstone is a complex task.
The Many AI Challenges of Hearthstone
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.
Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space
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.
Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network
Generative adversarial networks (GANs) are quickly becoming a ubiquitous approach to procedurally generating video game levels.
Video Game Level Repair via Mixed Integer Linear Programming
Recent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples.
Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding
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.
Watts: Infrastructure for Open-Ended Learning
This paper proposes a framework called Watts for implementing, comparing, and recombining open-ended learning (OEL) algorithms.
Language Model Crossover: Variation through Few-Shot Prompting
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.
Resource-constrained knowledge diffusion processes inspired by human peer learning
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.
