Procedural Content Generation (PCG) has become more common in usage in game development nowadays, with the motivation of finding new ways to make games replayable, new ways for games to be played or for generating content during development. This paper explores the question how often Procedural Generation is used in practice and furthermore how often it is used by different game genres and how they use PCG in their particular games. This paper will try to answer these questions through both an industry review, discovering which games have used Procedural Generation and also through a literature study to find out what kind of research has been done within the area ofPCG and how Game Developers could utilize that in the future. The findings were that even if the usage of PCG differentiated between genres, certain areas like Level Generationand entity instancating were more commonly using Procedural Generation compared with others such as Puzzle generation, Plot generation and Dynamic Systems. The literature study gave a perspective that there are plenty of research done within PCG on how to create new, different and unique ways to generate content, but it is usually in forms of prototypes and not ready to be used in games yet.This gives the conclusion that game genres use Procedural Generation to maximize the user experience with what the game wants out from that genre or use it to make game development more efficient. However, certain genres such as Adventure-games and Role-playing-games could benefit from having PCG for parts of the games where it is not used today which means there is still room for potentially using Procedural Generation. But with that also comes a discussion about what areas of PCG can be improved to meet the needs of the developers and make them more willing to use PCG on areas where it is not currently used.

Procedural Content Generation is now used in many games to generate a wide variety of content. It often uses players controlled by Artificial Intelligence for its evaluation. PCG content can also be used when training AI players to achieve better generalization. In both of these fields, evolutionary algorithms are employed, but they are rarely used together. In this thesis, we use the coevolution of AI players and level generators for platformer game Super Mario. Coevolution's benefit is, that the AI players are evaluated by adapting level generators, and vice versa, level generators are evaluated by adapting AI players. This approach has two results. The first one is a creation of multiple level generators, each generating levels of gradually increased difficulty. Levels generated using a sequence of these generators also mirror the learning curve of the AI player. This can be useful also for human players playing the game for the first time. The second result is an AI player, which was evolved on gradually more difficult levels. Making it learn progressively may yield better results. Using the coevolution also doesn't require any training data set.

Procedural content generation is a method that is sometimes used in video games to increase their replayability. In our previous work (Nepožitek, 2018), we implemented an algorithm for procedural generation of 2D dungeons, with the main focus on giving game designers complete control over the structure of generated levels. The algorithm takes a set of user-defined building blocks as input and produces levels that all follow the structure of a specified level connectivity graph. In the first part of the thesis, we address some shortcomings of our previous work. We improve the algorithm with several new features such as better support for corridors between rooms or the possibility to generate platformer levels. We also propose several performance improvements and analyze the speed of the algorithm on various inputs. In the second part of the thesis, we present an integration of our algorithm into the Unity game engine. In the final part of the thesis, we demonstrate that our generator is able to produce levels that are similar to what we can see in two popular games - Enter the Gungeon and Dead Cells. The resulting algorithm is much faster than the previous version, contains new features and is ready to be used in the Unity game engine.

Context. Creating resources for games and 3D environments is an effort consuming process. Some are looking to procedural algorithms to aid in this endeavour but the effort to configure the algorithms can be time consuming in itself. This paper will continue from a set of papers written by Frade et al. where they surrender the process of configuration to the algorithm by using genetic optimization together with a set of fitness functions. This is then tested on procedural generation of height maps.Objectives. The original algorithm utilizes a tree of functions that generates height maps using genetic optimization and a set of fitness functions. The output of the original algorithm is highly dependent on a specic noise function.This paper will investigate if the inverse Fourier transform can be used as an intermediate phenotype in order to decrease the relationship between the set of functions in the algorithm and the types of output.Methods. A reference implementation was first produced and verified. The Fourier transform was then added to the algorithm as an intermediate phenotype together with improvements on the original algorithm. The new algorithm was then put to the test via five experiments, where the output was compared with the reference implementation using manual review.Results. The implementation of Fourier transform that was attempted in this paper exclusively produced noisy output.Conclusions. The modified algorithm did not produce viable output. This most likely due to the behaviour of the Fourier transform in itself and in relation to the implementation of fitness calculation.

Manual design of levels can be an expensive and time consuming process. Procedural content generation (PCG) entails methods to algorithmically generate game content such as levels. One such way is by using cellular automata (CA), and in particular evolved cellular automata. Existing research primarily considers specifically determined starting states, as opposed to randomly initialized ones. In this paper we investigate the current state of the art regarding using CA’s that have been evolved with a genetic algorithm (GA) for level generation purposes. Additionally, we create a level generator that uses a GA in order to evolve CA rules for the creation of maze-like 2d levels which can be used in video games. Specifically, we investigate if it is possible to evolve CA rules that, when applied to a set of random starting states, could transform these into game levels with long solution paths and a large number of dead ends. We generate 60 levels over 6 experiments, rendering 58 playable levels. Our analysis of the levels show some flaws in certain levels, such as large numbers of unreachable cells. Additionally, the results indicate that the designed GA can be further improved upon. Finally, we conclude that it is possible to evolve CA rules that can transform a set of random starting states into game levels.

Procedural Content Generation (PCG) in games often suffers from a lack of emotional connection with the player as well as a perceived sameness. This thesis aims to design and test a tool for PCG game levels for the game To the Skies! which incorporates an emotional intention based upon the color-emotion theory proposed by Fugate and Franco, 2019.  Russell’s (1980) Circumplex Model of Affect was used as a foundation for the parameterization of emotions, which were implemented in a PCG test level for the game, developed in Unreal Engine. By adopting research through design, this thesis goes through an iterative process where the tool is under constant improvement through quantitative testing using the Self Assessment Manikin method proposed by Lang & Bradley (1994). The tests indicate that, although colors can to some degree elicit emotions, the player’s perception of the test levels did not match the expected emotional response based on color alone for all emotional states tested. The thesis concludes that, while color-emotion association can aid in generating PCG with emotional intent, additional complementary elicitors are likely required, and further tests are needed that incorporate shapes, placement, and weather patterns.

Background: Procedural content generation (PCG) has become an important subject as the demand for content in modern games has increased. Paradox Arctic is a game development studio that aims to be at the forefront of technological solutions and is therefore interested in furthering their knowledge in PCG. To this end, Paradox Arctic has expressed their interest in a collaborative effort to further explore the subject of procedural floor plan generation. Objective: The main goal of this work is to test whether a solution based on growth, subdivision or a combination thereof, can be used to procedurally generate believable and varied floor plans for game environments, while also conforming to predefined constraints. Method: A solution capable of generating floor plans with the use of growth, subdivision and a combination of both has been implemented and a survey testing the believability and variation of the generated layouts has been conducted. Results & Conclusions: While the results of the subdivision and combined solutions show that more work is necessary before the generated content can be considered believable, the growth based solution presents promising results in terms of believability when generating smaller to medium sized layouts. This believability does however come at the cost of variation. / Bakgrund: Procedural content generation (PCG) har blivit ett alltmer viktigt ämne allteftersom kravet på mängden innehåll i moderna spel har ökat. Paradox Arctic är en spelutvecklingsstudio vars målsättning är att ligga i teknologins framkant och de är därför intresserade av att vidareutveckla sin kompetens inom PCG. Av denna anledning har de uttryckt intresse för ett samarbete inom området “procedurell generering av planlösningar”. Syfte: Det huvudsakliga syftet med detta arbete är att undersöka huruvida lösningar baserade på att växa ytor, fördela ytor i mindre delar eller en kombination av dessa, kan användas för att skapa trovärdiga och varierade planlösningar för spelmiljöer, utan att bryta förutbestämda krav. Metod: En lösning som procedurellt genererar planlösningar genom att växa och/eller fördela dem har implementerats och en undersökning, med syftet att utvärdera trovärdigheten och variationen i de genererade planlösningarna, har utförts. Resultat & Slutsatser: Lösningen som baseras på fördelning av ytor och den kombinerade lösningen, visades av resultaten kräva ytterliggare arbete för att anses generera trovärdiga resultat. Lösningen som baseras på att växa ytor däremot, visade positiva trovärdighetsresultat när små och medelstora planlösningar genererades. Detta goda resultat uppstod dock på bekostnaden av variation mellan de genererade planlösningarna.

This research evaluates two-dimensional top-down dungeon generated levels regarding fundamental and micro dungeon design patterns. Additionally, it investigates the meaningfulness of the evaluation results in terms of accessibility to level designers and similar. The research method concentrates on two dungeon-generation techniques – Cellular Automata and Drunkard Walk. Each generated level gets evaluated based on three evaluation stages that build on top of each other: the passability of each tile; categorization of each collection of tiles with the same attributes; and player-centric gameplay data. The results show key differences between Cellular Automata and Drunkard Walk as the risk of using Cellular Automata to generate up to 90% unreachable space, while drunkard walk always has a playable relative space size of 100%. The evaluation also shows results that depend on the requirements of a game or constraints of a level designer. Cellular Automata generates more rooms, while Drunkard Walk provides more decisions per room. In conclusion, the evaluation results show differences between the two algorithms, presented using a vocabulary that is familiar to a level designer.

Procedural Content Generation (PCG) is a tool for developers to take advantage of the computational power of the computer in order to create new content. There are many different method that a PCG program is able to utilize but finding the most optimal may be very challenging. In this paper we improved Evolution Dungeon Designer (EDD) by integrate symmetry and similar fitness function. We evaluated them with experiments and a user study that involved participants that are active in the field of game development. We can see that both symmetry and similar functions can easily be integrated for creating 2D dungeon rooms but has the potential of overwhelm the existing functions.

An Evolutionary Algorithm was run in real-time for the procedural generation ofenemies in a third-person, wave based hack and slash and shoot 'em up game. Thealgorithm evaluates enemies as individuals based on their effectiveness at battlingthe player character. Every generation is presented as a new wave of enemieswhose properties have been adjusted according to the fitness of the last wave. Byconstantly making new enemies more adept at the task of the defeating the currentplayer, I attempt to automatically and naturally raise the difficulty as the gameprogresses. The goal is also to improve player satisfaction as a result. By analyzingthe response from players and observing the changes of the generated enemies, Idetermine whether or not this is an appropriate implementation of EvolutionaryAlgorithms. Results showed that the success of the algorithm varied substantiallybetween tests, giving a number of both failed and successful tests. I go throughsome of the individual data and draw conclusions on what specific conditions makesthe algorithm perform desirably.