https://www.um.edu.mt/library/oar/handle/123456789/84916 Mon, 13 Apr 2026 03:57:26 GMT 2026-04-13T03:57:26Z https://www.um.edu.mt/library/oar/handle/123456789/108078 Title: An inquiry into MOBAs as a space for players with social anxiety disorder Abstract: Activity Theory is a framework used to understand human activities as a systematic and socially situated phenomena. This framework is known for being a descriptive metatheory and tool, rather than a predictive framework. This framework emphasises the use of tools or artefacts where computing technologies are central mediators of human experience, meaning it gives attention to human-computer interaction. Using Activity Theory, I will attempt to apply this framework within the field of Game Studies, simultaneously bringing in psychological approaches and concepts such as behaviourism, cognitive behaviourism and stress and anxiety related disorders. Psychological concepts are addressed in order to discuss how players with Social Anxiety Disorder can engage with others in goal-directed interactions within competitive team based games. I will attempt to present a hypothesis stating the significance of virtual and online environments within videogames as it can provide a safe space within moderation, for the player to act authentically with the help of the distraction of having a common goal with other people in the community. According to this framework, players with social anxiety disorder can transform their problematic behaviour and develop a better attitude by challenging their social anxiety both consciously and unconsciously. Using Yrjö Engestrom’s model, I will propose and explain how the framework can prevent escalations of anxiety and depression as it accounts for the environment, action, motivations and the role of the tool or technological artefact being the video game itself. This model can also be understood as a process of learning for players with social anxiety disorder and thus it is necessary to discuss the range of factors within the model which together impact an activity arbitrated by the tool or videogame and by the community and its rules. I consider the Activity Theory framework as applied to Game Studies and Social Anxiety Disorders, to be beneficial in providing a theoretical compass to recognise and confront atypical behaviour and efficiently carry-out ones’ interests and goals. Description: M.Sc.(Melit.) Fri, 01 Jan 2021 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/108078 2021-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/107911 Title: Combining wave function collapse and evolutionary algorithms for controlled content generation Abstract: Wave Function Collapse (WFC) is a procedural content generation algorithm introduced by Maxim Gumin that has risen in popularity in recent years. The Wave Function Collapse algorithm utilizes two distinct models, the Overlapping and the Simple Tiled model, to divide input bitmap-based or tiled-based images into patterns of various shapes and consistently generate output images of a larger scale, which feature the same patterns. Although WFC is able to create images that are visually stunning in massive numbers, there hasn’t been many attempts to generalize it, in order to make it able to be applied for game content generation. Specifically, the images that are generated can be used as textures for games and sometimes as levels, but the playability of these levels is not guaranteed. In this thesis, WFC is combined with an evolutionary algorithm in an attempt to control the generated outputs of WFC and push them towards a more playable nature. The implemented algorithm evolves patterns, in the form of tiled images, that will then be used as input for the WFC algorithm. The idea is to first create visually flawless images through an evolutionary procedure, using a given tileset, that will result in the generation of similarly flawless images and then elaborate further so that some control over the generated content of the WFC algorithm is established. First, we go through every parameter of our evolutionary algorithm, like the selection method, the fitness function, the genetic operators and the population size, exploring the impact that each of them can have on the produced results and then we propose an optimal setup for our approach. The proposed setup managed to have a very promising performance and within a reasonable amount of computational resources. Furthermore, the algorithm managed to maintain the same performance when tested on totally random tilesets, while the results that were being produced through the WFC algorithm were increasing in complexity for larger tilesets, while maintaining the algorithm’s wide expressive range. Finally, despite the good performance of our implementation, there is definitely some room for improvement. In this approach we explored many of the parameters that can have an impact on the evolution of the input patterns, but there is still much research to be done in determining the best approach. Alternatively, this approach can be utilized by future implementations that want to address similar problems. Description: M.Sc.(Melit.) Fri, 01 Jan 2021 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/107911 2021-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/107908 Title: In it for the money : monetization design in casual games Abstract: Casual games are a global phenomenon that is constantly shifting. While hardcore games gather most of the attention in game studies, casual games and especially the monetization angle are sidelined. This study aims to fill this gap in the research and reignite the discussion around casual games. Specifically, it tackles the extent to which monetization should be considered a fundamental element of the casual game definition. Monetization is defined as the practices that allow the game to generate revenue for the publisher. The methodology consisted of closely studying and analyzing five casual games using game analysis, game design studies, and business studies. The results showed that the closer to a freemium or free-to-play business model the game was, the more monetization directly affected the game design. It also revealed a new type of game, Hyper-casual games, a category until now relatively undefined in the context of game studies. These results suggest that monetization is an influential factor in the game design and, therefore, when defining a casual game. Also, it showed that casual games ought to be defined as the set of qualities and intent of the components of the game in their goal to reach a mass market. Description: M.Sc.(Melit.) Fri, 01 Jan 2021 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/107908 2021-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/107901 Title: Deep learning novelty exploration for minecraft building generation Abstract: Computational creativity (CC) refers to the study of computational systems that exhibit behaviors that an unbiased observed would consider creative. CC systems have shifted focus toward intrinsic motivation (IM) and open-endedness (OE), which are at the heart of creative behavior in biological systems. In this project, we apply these concepts to a procedural content generator that autonomously creates Minecraft buildings according to its own evolving definition of novelty. This work addresses a research gap in PCG, specifically in Minecraft, which currently focuses on generating adaptive settlement layouts without prioritizing creativity. This system follows the fundamentals of the DeLeNoX algorithm. An autoencoder identifies the high-level features of buildings, compressing them into onedimensional latent vectors. The system alternates between phases of exploration and transformation. In exploration, CPPN-NEAT is used to evolve populations of buildings using constrained novelty search. We calculate an individual’s novelty as the average euclidean distance to the nearest K neighbors in the latent space. In transformation, the autoencoder is retrained with a dataset of the most novel individuals created in the previous exploration phase/s. We experiment with different approaches to the retraining of the autoencoder and observe their impact on the diversity and complexity of the content generated. We assess the results quantitatively by comparing population diversities across experiments, and by visualizing their expressive range using a set of building properties. Finally, we compare the structures qualitatively and observe the effective change in complexity in the structures over time. Our results show that the transformation phase is most effective when it uses larger training sets and includes examples from all previous iterations of the algorithm. This allows the system to more effectively scale in effective complexity of building features, which become more similar to examples of realistic buildings over time. Description: M.Sc.(Melit.) Fri, 01 Jan 2021 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/107901 2021-01-01T00:00:00Z