Due to the reverse engineering of the file format for the available level editor, the generations upon Hotline Miami demonstrated in this paper can be used in the working commercial game, and the file format is described allowing for others to also demonstrate other techniques in this game. However, none of these examples are using the commercial game’s own engine as part of the tool chain and instead require a complete reimplementation of the game. Noteworthy examples of PCG for commercially available games include: Ropossum levels, Mario Levels, a generator for Zelda levels, and Spelunky generation. Yet, very few academic generative methods have been applied with much success to current games and methods in the industry, in part due to trade secrets employed by developers. The system creates what the authors call a multi-faceted evolution which procedurally generates a map, a layout defined as the player and NPC positions, and a rule-set for the game. Cook and Colton build an entire arcade game using various PCG techniques as presented above for a framework known as ANGELINA. Small example games have shown how PCG integration can be used to create an entire game. uses Genetic Algorithms as a placement method for title in a serious game, and demonstrate that they outperform humans on relatively simple placement tasks compared with an objective measure of fitness. The fitness functions used include a placement of ‘event rooms’ which could contain boss battles or treasure rooms. The tile method follows a number of current industry practices of the creation of random levels with game elements. Valtchanov and Brown use a genetic programming approach to create Diablo style levels via the placement of pre-generated tiles. This technique could be extended to include other generation types, e.g., terrain, in order to create large maps. McGuinness and Ashlock showed that the resulting mazes generated in the previous work could be tiled in order to create larger, more complex mazes. Ashlock et al.’s studies use a checkpoint-based genetic algorithm that optimizes the fitness function toward creating mazes with user-defined characteristics. Many representations were introduced that allow the designer control over the type of mazes that are generated, e.g., caverns, rooms, and etc.
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