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Found 9 papers, 6 papers with code
A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences
Optimizing discrete black-box functions is key in several domains, e. g. protein engineering and drug design.
A Continuous Relaxation for Discrete Bayesian Optimization
To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization.
MarioGPT: Open-Ended Text2Level Generation through Large Language Models
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
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.
Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry
Deep generative models can automatically create content of diverse types.
Variational Neural Cellular Automata
Additionally, we show that the VNCA can learn a distribution of stable attractors that can recover from significant damage.
Pulling back information geometry
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
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
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.
