Endless runner (ER) is a game genre where the player controls a constantly running character. The player's enjoyment is closely tied to the difficulty of the game, which makes it an interesting platform for dynamic difficulty adjustment (DDA). DDA is a way of balancing game's difficulty by the use of computer-aided adjusting methods. First, we have developed an endless runner type of game using Unity and utilizing client-server architecture. Second, we have implemented a DDA system using player modeling and genetic algorithms. We have tested the validity of our approach on live users. We were able to adjust the game difficulty to increase player enjoyment and reduce player death rates in levels. This approach can be used in a production environment to improve players' enjoyment of endless runner games.

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.

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.

Games get boring when they start repeating themselves and do not offer players new content. Procedural content generation (PCG) is increasingly used to generate this content. PCG-based game design decreases the need to have a human designer or a writer to generate the content. Algorithmic creation of game content can augment the creativity of human designers and this makes it possible small so-called indie teams to create the content for their game without the big resources. In this work, the field of PCG is introduced. Application of PCG is shown through a mobile game implementation. The implementation details of the mobile game Rush Hour will be presented that makes use of Foursquare, Twitter and Mapbox APIs, which eases the content creation using open data as the input of PCG.