The aim of this thesis is to evaluate the use of Search-based Procedural Content generation (SBPCG) to help a designer create levels for different game styles. I show how SBPCG can be used for level generation in different game genres by surveying both paper and released commercial solutions. I then provide empirical data by using a Genetic Algorithm (GA) to evolve levels in two different game types, first one being a space puzzle game, and the second a platform game. Constraints from a level designer provide a base to create fitness functions for both games with success. Even though difficulties with level representation make it hard for a designer to work with this technique directly, the generated levels show that the technique has promising potential to aid level designers with their work.

Games are irritating when they are too hard, and boring when they are too easy. Level Eight is a game development company which is, during this project, developing an endless runner. This thesis describes research and implementation of a dynamic difficulty adjustment system in Level Eights’ endless runner by considering several conditions of the player. This was accomplished with the help of the game environment by implementing procedural content generation and combine it with the dynamic difficulty adjustment system. / Spel är irriterande när de är för svåra, och tråkiga när de är för lätta. Level Eight är ett spelutvecklingsföretag som under detta projekt utvecklar ett ändlöst löparspel. Denna avhandling beskriver forskning och genomförande av ett dynamiskt svårighets justeringssystem i Level Eights ändlöst löparspel genom att överväga flera villkor för spelaren. Detta uppnåddes med hjälp av spelmiljön genom att implementera procedurell innehållsgenerering och kombinera den med det dynamiska svårighets justering systemet.

Submitted by Marco Antônio de Ramos Chagas (mchagas@ufv.br) on 2016-09-09T18:25:14Z No. of bitstreams: 1 texto completo.pdf: 2101606 bytes, checksum: c4227b09a3bae62b835f5aabafc917b3 (MD5) / Made available in DSpace on 2016-09-09T18:25:14Z (GMT). No. of bitstreams: 1 texto completo.pdf: 2101606 bytes, checksum: c4227b09a3bae62b835f5aabafc917b3 (MD5) Previous issue date: 2016-05-25 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Neste trabalho apresentamos um modelo computacional baseado em teorias de design para gerar mapas de jogos de plataforma visualmente agradáveis. Nós estudamos o problema de geração de mapas como um problema de otimização e provamos que uma versão simplificada do problema é computacionalmente difícil. Em seguida, propomos uma abordagem de busca heurística para resolver o problema de geração de mapas e utilizamos ela para gerar níveis de um clone do Super Mario Bros (SMB), chamado Infinite Mario Bros (IMB). Antes de avaliar os níveis de IMB gerados pelo nosso sistema, realizamos um estudo detalhado das abordagens comumente utilizadas para avaliar o conteúdo gerado por programas de computador. A avaliação utilizada em trabalhos anteriores utiliza apenas métricas computacionais. Embora esses indicadores são importantes para uma avaliação inicial e exploratória do conteúdo gerado, não é claro se são capazes de capturar a percepção do jogador sobre o conteúdo gerado. Neste trabalho, comparamos os conhecimentos adquiridos a partir de um estudo com seres humanos usando níveis de IMB gerados por diferentes sistemas, com os conhecimentos adquiridos a partir de análise dos valores de métricas computacionais. Os nossos resultados sugerem que as m ́etricas computacionais atuais não devem substituir estudos com seres humanos para avaliar o conteúdo gerado por programas de computador. Usando os conhecimentos adquiridos em nosso experimento anterior, foi realizado outro estudo com seres humanos para avaliar os níveis de IMB gerados pelo nosso método. Os resultados mostram a vantagem do nosso método em relação a outras abordagens em termos de estética visual e diversão. Finalmente, foi realizado outro estudo com seres humanos, mostrando que o nosso método é capaz de gerar níveis de IMB semelhantes aos níveis de SMB criados por designers profissionais. / In this work we introduce a computational model based on theories of graphical design to generate visually pleasing video game maps. We cast the problem of map generation as an optimization problem and prove it to be computationally hard. Then, we propose a heuristic search approach to solve the map generation problem and use it to generate levels of a clone of Super Mario Bros (SMB) called Infinite Mario Bros (IMB). Before evaluating the levels of IMB generated by our system, we perform a detailed study of the approaches commonly used to evaluate the content generated by computer programs. The evaluation used in previous works often relies on computational metrics. While these metrics are important for an initial exploratory evaluation of the content generated, it is not clear whether they are able to capture the player’s perception of the content generated. In this work we compare the insights gained from a user study with IMB levels generated by different systems with the insights gained from analyzing computational metric values. Our results suggest that current computational metrics should not be used in lieu of user studies for evaluating content generated by computer programs. Using the insights gained in our previous experiment, we performed another user study to evaluate the IMB levels generated by our method. The results show the advantage of our method over other approaches in terms of visual aesthetics and enjoyment. Finally, we performed one last user study that showed that our method is able to generate IMB levels with striking similarity to SMB levels created by professional designers. / Autor sem Lattes e arquivo PDF não criptografou

Procedural content generation (PCG) is increasingly used to generate many aspects in a variety of games. AI players, both hand scripted or also generated (by AI methods), are used to evaluate this content. Comparatively little effort is invested in using PCG to generate the whole game, including its rules. In this thesis, we use evolutionary algorithms to generate the game rules, its content and the evaluating AI player on a narrow, but flourishing, genre of endless runners - games where the player is constantly running. For this purpose, we have implemented a framework for creating endless runner games. Our approach could provide more efficiency for game designers, explore completely new game concepts in endless runners, platformer games, and be further generalized to other game genres.

Context. Game development is a costly process and with today's advanced hardware the customers are asking for more playable content, and at higher quality. For many years providing this content procedurally has been done for level creation, modeling, and animation. However, there are games that require content in other forms, such as executable quests that progress the game forward. Quests have been procedurally generated to some extent, but not in enough detail to be usable for game development without providing a handwritten description of the quest. Objectives. In this study we combine a procedural content generation structure for quests with a natural language generation approach to generate a descriptive summarized text for quests, and examine whether the resulting texts are viable as quest prototypes for use in game development. Methods. A number of articles on the area of natural language generation is used to determine an appropriate way of validating the generated texts produced in this study, which concludes that a user case study is appropriate to evaluate each text for a set of statements. Results. 30 texts were generated and evaluated from ten different quest structures, where the majority of the texts were found to be good enough to be used for game development purposes. Conclusions. We conclude that quests can be procedurally generated in more detail by incorporating natural language generation. However, the quest structure used for this study needs to expand into more detail at certain structure components in order to fully support an automated system in a flexible manner. Furthermore due to semantics and grammatics being key components in the flow and usability of a text, a more sophisticated system needs to be implemented using more advanced techniques of natural language generation.

The modeling of trees is challenging due to their complex branching structures. Three different ways to generate trees are using real world data for reconstruction, interactive modeling methods and modeling with procedural or rule-based systems. Procedural content generation is the idea of using algorithms to automate content creation processes, and it is useful in plant modeling since it can generate a wide variety of plants that can adapt and react to the environment and changing conditions. This thesis focuses on and extends a procedural tree generation technique that uses a space colonization algorithm to model the tree branches' competition for space, and shifts the previous works' focus from realism to fantasy. The technique satisfied the idea of using interaction between the tree's internal and external factors to determine its final shape, by letting the designer control the where and the how of the tree's growth process. The implementation resulted in a tree generation application where the user's imagination decides the limit of what can be produced, and if that limit is reached can the application be used to randomly generate a wide variety of trees and tree-like structures. A motivation for many researchers in the procedural content generation area is how it can be used to augment human imagination. The result of this thesis can be used for that, by stepping away from the restrictions of realism, and with ease let the user generate widely diverse trees, that are not necessarily realistic but, in most cases, adapts to the idea of a tree.

Computer games have always become more and more advanced. One of the biggest reasons to its rapid evolution is the use of procedural content generation (PCG), which is used to generate game content automatically. However, there is one type of games that is more unexplored when it comes to PCG, namely motion games. Motion games are games where the player interacts with the game by moving his own body rather than using a gamepad, mouse or keyboard. Thanks to that, motion games are a healthier alternative to regular games, and this thesis therefore explores the possibilities to use PCG to develop more exciting motion games. The focus lies on achieving variation and progression since both of those are important concepts closely related to PCG. An exploratory case study is also conducted to examine how the derived guidelines work in a real game. The result concluded is that the guidelines seem to work well, but that all of them are not easily adapted to every game. Different game genres therefore call for different guidelines to be used.

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.