text: Generative Adversarial Networks (GANs) are a machine learning approach capable of generating novel example outputs across a space of provided training examples. Procedural Content Generation (PCG) of levels for video games could benefit from such models, especially for games where there is a pre-existing corpus of levels to emulate. This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus. The approach successfully generates a variety of levels similar to one in the original corpus, but is further improved by application of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Specifically, various fitness functions are used to discover levels within the latent space of the GAN that maximize desired properties. Simple static properties are optimized, such as a given distribution of tile types. Additionally, the champion A* agent from the 2009 Mario AI competition is used to assess whether a level is playable, and how many jumping actions are required to beat it. These fitness functions allow for the discovery of levels that exist within the space of examples designed by experts, and also guide the search towards levels that fulfill one or more specified objectives.
  @cite_1: 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 (such as board games). The term search-based procedural content generation is proposed as the name for this emerging field, which at present is growing quickly. A taxonomy for procedural content generation is devised, centering on what kind of content is generated, how the content is represented and how the quality fitness of the content is evaluated; search-based procedural content generation in particular is situated within this taxonomy. This article also contains a survey of all published papers known to the authors in which game content is generated through search or optimisation, and ends with an overview of important open research problems.
  @cite_10: 
  @cite_13: 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 through discussing some properties of PCG which are sometimes thought of as necessary but are actually not. After drawing up some clear demarcations for what is and what is not PCG, we present two versions of a content generation system for Infinite Mario Bros which is intentionally designed to question these same demarcations. We argue that, according to our own definition, one version of the system is an example of PCG while the other is not, even though they are mostly identical. We hope that this paper answers some questions but raises others, and inspires researchers and developers to thread some less common ground in developing content generation techniques.
  @cite_8: This chapter discusses the challenges and opportunities of procedural content generation (PCG) in games. It starts with defining three grand goals of PCG, namely multi-level multicontent PCG, PCG-based game design and generating complete games. The way these goals are defined, they are not feasible with current technology. Therefore we identify nine challenges for PCG research. Work towards meeting these challenges is likely to take us closer to realising the three grand goals. In order to help researchers get started, we also identify five actionable steps, which PCG researchers could get started working on immediately.
summary: Togelius @cite_13 defined Procedural Content Generation (PCG) as the @cite_13 @cite_1 @cite_8 . Examples of game content include game rules, levels, maps mazes, characters, weapons, vehicles, background stories, textures and sound. Automatic game level generation, with little or no human intervention, is a challenging problem. For some games, the levels are represented as maps or mazes @cite_10 . Examples include , , and , one of the classic platform video games created by .