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.
The first academic Procedural Content Generation competition was the 2010 Mario AI Championship @cite_0 , in which the participants were required to submit a level generator which implements a provided Java interface and returns a new level within @math seconds. The competition framework was implemented based on https: tinyurl.com yan4ep7g , a public clone of .
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The Level Generation Competition, part of the IEEE Computational Intelligence Society (CIS)-sponsored 2010 Mario AI Championship, was to our knowledge the world's first procedural content generation competition. Competitors participated by submitting level generators - software that generates new levels for a version of Super Mario Bros tailored to individual players' playing style. This paper presents the rules of the competition, the software used, the scoring procedure, the submitted level generators, and the results of the competition. We also discuss what can be learned from this competition, both about organizing procedural content generation competitions and about automatically generating levels for platform games. The paper is coauthored by the organizers of the competition (the first three authors) and the competitors. We give a brief overview of the Mario AI Championship, a series of competitions based on an open source clone of the seminal platform game Super Mario Bros. The competition has four tracks. The gameplay and learning tracks resemble traditional reinforcement learning competitions, the Level generation track focuses on the generation of entertaining game levels, and the Turing Test track focuses on humanlike game-playing behavior. We also outline some lessons learned from the competition and its future. The article is written by the four organizers of the competition. This paper describes the 2009 Mario AI Competition, which was run in association with the IEEE Games Innovation Conference and the IEEE Symposium on Computational Intelligence and Games. The focus of the competition was on developing controllers that could play a version of Super Mario Bros as well as possible. We describe the motivations for holding this competition, the challenges associated with developing artificial intelligence for platform games, the software and API developed for the competition, the competition rules and organization, the submitted controllers and the results. We conclude the paper by discussing what the outcomes of the competition can teach us both about developing platform game AI and about organizing game AI competitions. The first two authors are the organizers of the competition, while the third author is the winner of the competition. Summary: ImgLib2 is an open-source Java library for n-dimensional data representation and manipulation with focus on image processing. It aims at minimizing code duplication by cleanly separating pixel-algebra, data access and data representation in memory. Algorithms can be implemented for classes of pixel types and generic access patterns by which they become independent of the specific dimensionality, pixel type and data representation. ImgLib2 illustrates that an elegant high-level programming interface can be achieved without sacrificing performance. It provides efficient implementations of common data types, storage layouts and algorithms. It is the data model underlying ImageJ2, the KNIME Image Processing toolbox and an increasing number of Fiji-Plugins. Availability: ImgLib2 is licensed under BSD. Documentation and source code are available at http: imglib2.net and in a public repository at https: github.com imagej imglib. Supplementary Information: Supplementary data are available at Bioinformatics Online. Contact: ed.gbc-ipm@dleflaas