Recent years have seen a rise in the popularity of quality diversity (QD) optimization, a
branch of optimization that seeks to find a collection of diverse, high-performing solutions to …

In many text-generation problems, users may prefer not only a single response, but a
diverse range of high-quality outputs from which to choose. Quality-diversity (QD) search …

Recent progress in reinforcement learning (RL) has started producing generally capable
agents that can solve a distribution of complex environments. These agents are typically …

We study the problem of generating arbitrarily large environments to improve the throughput
of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effective …

With the rapid progress in Multi-Agent Path Finding (MAPF), researchers have studied how
MAPF algorithms can be deployed to coordinate hundreds of robots in large automated …

We are at the cusp of a transition from 'learning from data'to 'learning what data to learn
from'as a central focus of artificial intelligence (AI) research. While the first-order learning …

Single-objective optimization algorithms search for the single highest-quality solution with
respect to an objective. Quality diversity (QD) optimization algorithms, such as Covariance …

Quality Diversity (QD) algorithms such as MAP-Elites are a class of optimisation techniques
that attempt to find many high performing points that all behave differently according to a …

Evolutionary machine learning (EML) has been applied to games in multiple ways, and for
multiple different purposes. Importantly, AI research in games is not only about playing …

A Software Product Line (SPL) is a set of software products that are built from a variability
model. Real-world SPLs typically involve a vast number of valid products, making it …