Search Results for author: Miguel González-Duque

Found 7 papers, 4 papers with code

MarioGPT: Open-Ended Text2Level Generation through Large Language Models

1 code implementation NeurIPS 2023 Shyam Sudhakaran, Miguel González-Duque, Claire Glanois, Matthias Freiberger, Elias Najarro, Sebastian Risi

MarioGPT can not only generate diverse levels, but can be text-prompted for controllable level generation, addressing one of the key challenges of current PCG techniques.

Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds

no code implementations4 Oct 2022 Noémie Jaquier, Leonel Rozo, Miguel González-Duque, Viacheslav Borovitskiy, Tamim Asfour

This may be attributed to the lack of computational models that fill the gap between the discrete hierarchical structure of the taxonomy and the high-dimensional heterogeneous data associated to its categories.

Variational Neural Cellular Automata

1 code implementation ICLR 2022 Rasmus Berg Palm, Miguel González-Duque, Shyam Sudhakaran, Sebastian Risi

Additionally, we show that the VNCA can learn a distribution of stable attractors that can recover from significant damage.

Pulling back information geometry

1 code implementation9 Jun 2021 Georgios Arvanitidis, Miguel González-Duque, Alison Pouplin, Dimitris Kalatzis, Søren Hauberg

Latent space geometry has shown itself to provide a rich and rigorous framework for interacting with the latent variables of deep generative models.

Fast Game Content Adaptation Through Bayesian-based Player Modelling

no code implementations18 May 2021 Miguel González-Duque, Rasmus Berg Palm, Sebastian Risi

Current systems for DDA rely on expensive data mining, or on hand-crafted rules designed for particular domains, and usually adapts to keep players in the flow, leaving no room for the designer to present content that is purposefully easy or difficult.

Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error

1 code implementation15 May 2020 Miguel González-Duque, Rasmus Berg Palm, David Ha, Sebastian Risi

The approach can reliably find levels with a specific target difficulty for a variety of planning agents in only a few trials, while maintaining an understanding of their skill landscape.

Bayesian Optimization

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