Procedural content generation in video games has a long history. Existing procedural
content generation methods, such as search-based, solver-based, rule-based and grammar …

In the past decade, we have witnessed the rise of deep learning to dominate the field of
artificial intelligence. Advances in artificial neural networks alongside corresponding …

Many concepts have been proposed for meta learning with neural networks (NNs), eg, NNs
that learn to reprogram fast weights, Hebbian plasticity, learned learning rules, and meta …

Deep learning has transformed the way large and complex image datasets can be
processed, reshaping what is possible in bioimage analysis. As the complexity and size of …

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 …

We present a method of generating diverse collections of neural cellular automata (NCA) to
design video game levels. While NCAs have so far only been trained via supervised …

Cellular automata (CA) are a class of computational models that exhibit rich dynamics
emerging from the local interaction of cells arranged in a regular lattice. In this work we focus …

Abstract Neural Cellular Automata (NCAs) have been proven effective in simulating
morphogenetic processes, the continuous construction of complex structures from very few …

Abstract Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos.
However, they require a slow iterative optimization process to synthesize a single fixed-size …

It has recently been shown that reinforcement learning can be used to train generators
capable of producing high-quality game levels, with quality defined in terms of some user …