People enjoy encounters with generative software, but rarely are they encouraged to interact with, understand or engage with it. In this paper we define the term 'PCG-based game', and explain how this concept follows on from the idea of an AI-based game. We look at existing examples of games which foreground their AI, put forward a methodology for designing PCG-based games, describe some example case study designs for PCG-based games, and describe lessons learned during this process of sketching and developing ideas.
describe @cite_10 , as a platforming game with procedurally generated levels. By exploring the levels and choosing particular exits, the player can alter the parameters to the procedural generator, allowing them to explore the generative space through gameplay. This is the earliest example we are aware of in which a game is designed around a procedural generator with the explicit intention of giving a player control of the generator's output. This is perhaps the best-known and most explicit use of procedural generation as a game mechanic.
@cite_10
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Procedural content generation and design patterns could potentially be combined in several different ways in game design. This paper discusses how to combine the two, using automatic platform game level design as an example. The paper also present work towards a pattern-based level generator for Super Mario Bros. (SMB), which is based on an analysis of the levels of the original SMB game where we found 23 different patterns. Procedural Content Generation (PCG) can be a useful tool for aiding creativity in the process of designing game levels. Mixed-initiative level generation tools where a designer and an algorithm collaborate to iteratively generate game levels have been used for this purpose. However, it can be difficult for designers to work with tools that do not respond to the common language of games: game design patterns. We present the Evolutionary Dungeon Designer, the first step towards a mixed-initiative dungeon design tool which evolves dungeon rooms using game design patterns, as well as several metrics regarding the placement of treasures and enemies, in the fitness function of a genetic algorithm. Our results show that we are able to control the frequency, shape and type of design patterns, as well as properly place enemies and treasures in the generated rooms, using design pattern-related input parameters. 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. This paper presents a generative system for the automatic creation of video game levels. Our approach is novel in that it allows high-level design goals to be expressed in a top-down manner, while existing bottom-up techniques do not. We use the FI-2Pop genetic algorithm as a natural way to express both constraints and optimization goals for potential level designs. We develop a genetic encoding technique specific to level design, which proves to be extremely flexible. Example levels are generated for two different genres of game, demonstrating the system’s broad applicability.