Much of recent machine learning has focused on deep learning, in which neural network
weights are trained through variants of stochastic gradient descent. An alternative approach …

This paper surveys the current state of the art in Natural Language Generation (NLG),
defined as the task of generating text or speech from non-linguistic input. A survey of NLG is …

Georgios N. Yannakakis Julian Togelius Page 1 Artificial Intelligence and Games Georgios N.
Yannakakis Julian Togelius Page 2 Artificial Intelligence and Games Page 3 Georgios N …

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 …

This survey explores procedural content generation via machine learning (PCGML), defined
as the generation of game content using machine learning models trained on existing …

While the history of machine learning so far largely encompasses a series of problems
posed by researchers and algorithms that learn their solutions, an important question is …

Generative Adversarial Networks (GANs) are a machine learning approach capable of
generating novel example outputs across a space of provided training examples. Procedural …

We investigate how reinforcement learning can be used to train level-designing agents. This
represents a new approach to procedural content generation in games, where level design …

Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as
defined by behavior metrics. This simultaneous focus on quality and diversity with explicit …

Procedural content generation (PCG) refers to the practice of generating game content, such
as levels, quests or characters, algorithmically. Motivated by the need to make games …