We introduce an alternative method for open-world scene generation. In this thesis, Graph-based Wave Function Collapse (GWFC) is integrated with Space Colonization Algorithm (SCA) and used to place objects in an unstructured 3D environment. This combined algorithm, Space Colonization Graph-based Wave Function Collapse (SC-GWFC), leverages the constraint-based capabilities of GWFC and the ability of SCA to populate arbitrary 3D volumes. We demonstrate that objects of variable scale can be successfully used with SC-GWFC. Since this algorithm is run in an interactive environment, we demonstrate iterative modifications to a partially complete scene and incorporate PCG into a scene editing process. As part of the implementation, we also introduce our Scene Modeling Application for rendering and editing 3D scenes. This modeling application allows for editing and viewing constraints for our SC-GWFC scene generator. We evaluate the performance characteristics of SC-GWFC in the Scene Modeling Application to demonstrate that SC-GWFC can be used interactively. Through the application, users can specify adjacency requirements for objects, and SC-GWFC will attempt to place objects in patterns that respect these rules. We demonstrate the ability to place up to 5000 items on a terrain using our proposed SC-GWFC technique.

Detta arbete handlar om Procedural Content Generation (PCG) i form av algoritmer som skapar osymmetriska banor, och fokuserar på att ge en översikt av prestanda avseende tid, storlek och tillgänglighetsgaranti. Tre olika algoritmer testades på hur lång tid det tog att skapa en bana, och hur stora dess banor blev, och om dessa banor tillåter att man kan nå alla gängliga områden. En Agentbaserad algoritm, Cellular Automata och Diffusion-limited Aggregation (DLA) studerades avseende dess styrkor och svagheter. Efter experimentet så drogs slutsatsen att DLA var mest effektiv inom tid och garanti dock skapade den små banor. Cellular Automata lyckades skapa stora rum men kunde de inte godkännas på punkten garanti, och den tog för lång tid att köra. Den Agentbaserade algoritmen misslyckades att skapa banor överhuvudtaget.

No description available.

Video games often include various creatures within their games, these creatures often give the world in which they inhabit a richer experience for the player. Creating these creatures can be costly, to lower these costs procedural content generated creatures could be used instead. Creatures created with PCG will have a lower upfront cost but comes with the downside of less control without a large amount of testing. This is a problem, what we have tried to do is lessening the amount of time needed to achieve the desired level of perfection. In this paper we have tried to find patterns which will further help the use of procedural content generated creatures. The game Forest Moon will be introduced to gather data and to find pattern of how players interact from various visual cues. With the data we have gained further understanding on how players react and what kind of emotions they feel when certain visual cues are presented.

No description available.

The application of PCG to generate puzzles offers great value since their replayability is severely limited, requiring any game that employs them to produce many different puzzles. In this paper we propose a modified version of the progressive content generation approach to function as a mixed-initiative system, to create puzzles for the novel partially physics-based game \textit{Everyone Must Die}. Thus exploring the adaptability and usefulness of the progressive content generation approach for a unique type of puzzle game. Further the mixed-initiative system is explored in relation to how effectively it can generate puzzles with a specified difficulty, an issue many papers exploring puzzle generation neglect. This is explored by implementing and incorporating a PCG system by extending an existing puzzle editor featured in the game. The analysis is conducted with the help of a user study on the developers of the game by testing qualitative experiences with the system. The promising results are then discussed and concluded with suggestions for future work and improvements to the described system and its used approach.

Procedural Content Generation innebär att spelinnehåll automatiskt genereras för att dels både öka variationen i spel dels och minska arbetsbelastningen hos designers. Ett användningsområde för detta är rumbaserad bangenerering. Målet med detta  här arbetet är var att jämföra två algoritmer som gör just detta:; Binary Space Partitioning och Delaunay Triangulation. De kriterier som algoritmerna utvärderades på var tidseffektivitet, variation, likhet och möjligheten att nå alla rum. Resultatet visade att Binary Space Partitioning hade snabbare genereringstid samtidigt som Delaunay Triangulation gav utvecklaren mer valmöjligheter. Vilken algoritm som var att föredra ifall tidsaspekten inte bar mest tyngd blev helt en en mestadels subjektiv fråga där varje enskild utvecklares önskemål kommer påverka svaret. Ifall arbetet skulle fortsättas i framtiden skulle det vara intressant att utföra fler tester med flera olika mätvärden samt använda algoritmerna i ett spel och sedan påta låta spelare testare bedöma kvalitén hos banorna som genereras.

The use of procedural content generation is becoming more and more popular in the video game industry. With games such as Minecraft or No Man’s Sky we have seen the potential of PCG in video game creation but also its challenges. In fact, while the processing power and memory capabilities of our machines are unceasingly growing, human capability for content creation doesn’t seem to be able to follow the same pace. Game developers had then to come up with several techniques and methods that will help them generate lots of content for their games while still keeping a certain level of control on the output. Urbis Terram is a procedural city generation tool for Unity3D that allows the creation of complete cities to be used in video games or simulations made with this engine. The goal of this thesis is to tackle the technical challenge of designing and implementing a PCG tool that will help game developers to quickly generate terrains, road networks and allotment spaces for buildings and other urban areas. The goal is to have a unique complete city generation tool that can enable quick game design iterations and can be used to create complex virtual worlds.

No description available.

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.