The focus of this survey is on research in applying evolutionary and other metaheuristic
search algorithms to automatically generating content for games, both digital and nondigital …

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

Hundreds of millions of people play computer games every day. For them, game content—
from 3D objects to abstract puzzles—plays a major entertainment role. Manual labor has so …

Procedural content generation (PCG) is an increasingly important area of technology within
modern human-computer interaction (HCI) design. Personalization of user experience via …

Player modeling is the study of computational models of players in games. This includes the
detection, modeling, prediction and expression of human player characteristics which are …

Procedural content generators for games produce artifacts from a latent design space. This
space is often only implicitly defined, an emergent result of the procedures used in the …

In this paper, we show that personalized levels can be auto-matically generated for platform
games. We build on previ-ous work, where models were derived that predicted player …

We try to clarify the concept of procedural content generation (PCG) through contrasting it to
other forms of content generation in games with which it could easily be mistaken, and …

This paper describes the Mario AI benchmark, a game-based benchmark for reinforcement
learning algorithms and game AI techniques developed by the authors. The benchmark is …

This paper presents a reliable and efficient approach to procedurally generating level maps
based on the self-organization capabilities of cellular automata (CA). A simple CA-based …