Search Results for author:
Found 10 papers, 8 papers with code
PlotMap: Automated Layout Design for Building Game Worlds
Concretely, we present a system that leverages Reinforcement Learning (RL) to automatically assign concrete locations on a game map to abstract locations mentioned in a given story (plot facilities), following spatial constraints derived from the story.
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.
T-DominO: Exploring Multiple Criteria with Quality-Diversity and the Tournament Dominance Objective
Keeping only a single balanced solution in each MAP-Elites bin maintains the visual accessibility of the archive -- a strong asset for design exploration.
COIL: Constrained Optimization in Learned Latent Space: Learning Representations for Valid Solutions
Constrained optimization problems can be difficult because their search spaces have properties not conducive to search, e. g., multimodality, discontinuities, or deception.
Discovering Representations for Black-box Optimization
Our second insight is that this representation can be used to scale quality diversity optimization to higher dimensions -- but only if we carefully mix solutions generated with the learned representation and those generated with traditional variation operators.
Prediction of neural network performance by phenotypic modeling
This difference, the phenotypic distance, can then be used to situate these networks into a common input space, allowing us to produce surrogate models which can predict the performance of neural networks regardless of topology.
Weight Agnostic Neural Networks
We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training.
Data-Efficient Design Exploration through Surrogate-Assisted Illumination
Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs.
Data-efficient Neuroevolution with Kernel-Based Surrogate Models
Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms.
Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user.
