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_15: This paper presents the use of design grammars to evolve playable 2D platform levels through grammatical evolution (GE). Representing levels using design grammars allows simple encoding of important level design constraints, and allows remarkably compact descriptions of large spaces of levels. The expressive range of the GE-based level generator is analyzed and quantitatively compared to other feature-based and the original level generators by means of aesthetic and similarity based measures. The analysis reveals strengths and shortcomings of each generator and provides a general framework for comparing content generated by different generators. The approach presented can be used as an assistive tool by game designers to compare and analyze generators' capabilities within the same game genre.
  @cite_14: The procedural generation of video game levels has existed for at least 30 years, but only recently have machine learning approaches been used to generate levels without specifying the rules for generation. A number of these have looked at platformer levels as a sequence of characters and performed generation using Markov chains. In this paper we examine the use of Long Short-Term Memory recurrent neural networks (LSTMs) for the purpose of generating levels trained from a corpus of Super Mario Brothers levels. We analyze a number of different data representations and how the generated levels fit into the space of human authored Super Mario Brothers levels.
  @cite_7: 
summary: The availability and popularity of the Mario AI framework has led to several approaches for generating levels for . Shaker @cite_15 evolved Mario levels using Grammatical Evolution (GE). In 2016, Summerville and Mateas @cite_14 applied Long Short-Term Memory Recurrent Neural Networks (LSTMs) to generate game levels trained on existing Mario levels, and then improved the generated levels by incorporating player path information. This approach inspired a novel approach to level generation, in which new levels are generated automatically from a sketch of some desired path drawn by a human designer. Another approach that was trained using existing Mario levels is that of Jain @cite_7 , which trained auto-encoders to generate new levels using a binary encoding where empty (accessible) spaces are represented by 0 and the others (e.g., terrain, enemy, tunnel, etc.) by 1. Though this approach could generate interesting levels, the use of random noise inputs into the trained auto-encoder sometimes led to problematic levels. Additionally, because of the binary encoding, no distinction was made between various possible types of tiles.