And with the combination of other fields, more accurate results can be obtained. Using the automatic generation levels of the animation game method can quickly generate many different levels, saving labor costs in manual production. With the development of computer animation games, now many animation games use the automatic generation of animation game levels method to generate animation game content. We conclude that the approach looks to be a promising one despite some inconsistency in efficacy in alternative domains, and that of the algorithms tested Multiple Correspondence Analysis appears to perform the most effectively. In this work we assess the efficacy of a selection of algorithms across sets of levels for 2D tile-based games by investigating how much their respective generative space compressions correlate with level behavioral characteristics. We hope that this approach could be the basis for developing useful qualitative tools for comparing PCG systems to help designers better understand and optimize their generators. It is to this area that this paper aims to make a contribution, by exploring the utility of data compression algorithms in compressing the generative spaces of PCG systems. An area in which progress has been comparatively slow is the development of generalisable approaches for comparing alternative PCG systems, especially in terms of their generative spaces. The past decade has seen a rapid increase in the level of research interest in procedural content generation (PCG) for digital games, and there are now numerous research avenues focused on new approaches for driving and applying PCG systems. This way we aim to provide methods for the comparison of PCG approaches and eventually, increase the quality and practicality of generated content in industry. We discuss the purpose of each of the considered methods in the context of PCG and provide guidelines for the interpretation of the results received. To do so, we present and discuss three efficient analysis tools, namely diagonal walks, the estimation of high-level properties, as well as problem similarity measures. We will demonstrate in this paper that obtaining more information about the defined optimisation problem can substantially improve our understanding of how to approach the generation of content. Such problems are predominantly tackled by evolutionary algorithms. A special class of these methods, which is commonly known as search-based PCG, treats the given task as an optimisation problem. The term Procedural Content Generation (PCG) refers to the (semi-)automatic generation of game content by algorithmic means, and its methods are becoming increasingly popular in game-oriented research and industry. Acceptable performance of the proposed method is reflected in the results, which can inform the academia of PCG and contribute to the game industry. EDA is an evolutionary stochastic optimization method and the introduction of probabilistic modeling as one of the main features of EDA into this problem domain is a reliable way to mathematically apply human knowledge to the challenging field of content generation. In this paper, we use the Estimation of Distribution Algorithm (EDA) to optimize the task of PCG in digital video games. In a sense, procedural content generation (PCG) is a Non-deterministic Polynomial-Hard optimization problem in which specific metrics should be optimized. Conventional game development as a human product is not cost efficient and the need for more intelligent, advanced and procedural methods is evident in this field. The video game industry is no exception and the ever-increasing demand for bigger titles containing vast volumes of content has become one of the vital challenges for the content generation domain. The results presented in this paper, and the accompanying source code, is meant to become a benchmark against which to test new level generators and expressivity metrics.Ĭontent generation is one of the major challenges in the modern age. These metrics are shown to provide interestingly different characterizations of the level generators. To compare them, we have defined six expressivity metrics, of which two are novel contributions in this paper. In all, we compare the output of seven different level generators from the literature, based on different algorithmic methods, plus the levels from the original Super Mario Bros game. To remedy this, we have conducted a large-scale comparative evaluation of level generators for the Mario AI Benchmark, a research-friendly clone of the classic platform game Super Mario Bros. Understand, quantitatively or qualitatively, what makes one generator di�erent from another in terms of its output. Is a glut of content generators, each serving di�erent purposes and using a variety of techniques. Evaluation is an open problem in procedural content generation research.
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