The FireFly company was created on the idea of blind dating. Most dating apps need the user to have an extensive conversation through a chat box before settling for a date. The product of FireFly tries to remedy that by having the first conversation be face to face. Over ten weeks, we have built the FireFly Dating application. We started researching other dating applications and matching algorithms to decide the requirements for FireFly Dating. After this, we implemented these requirements from scratch. In the final product, users can register an account, like or dislike other users profiles to indicate preferences for the matching algorithm, enroll for a blind date, and get matched. The application also includes an automated emailing system and a full reporting and feedback system. Furthermore, the system allows for an Administrator to get an overview of the application’s data. The Administrator can insert, edit or archive Dating Establishments and Time Slots. As well as view user reports and ban users if necessary. The final application is a product that portrays the Product Owner’s vision in that dating should be easily accessible and offline. At the end of the project, together with the Product Owner, it can be concluded that the FireFly Dating application was successful.

The monumental goal of Artificial Intelligence (AI) is to model general solutions that can be applied to perform a variety of tasks that normally demand human intelligence to solve. Traditionally human game developers painstakingly design and tweak levels until achieving the precise output of their heart’s desire. In the gaming industry, AI for level generation can reduce the need for labour-intensive human design. A general game AI for level generation can only be created once we have a method to describe video games. Video Game Description Language (VGDL) is a high-level language for describing 2D arcade games that consists of two parts, a game and level description. Using this language allows us to analyze games at their mechanical level. The problem of General Video Game Level Generation (GVG-LG) can thus be defined as follows: construct a generator that, given a game (e.g. described in VGDL) which can be played by some AI player, builds any required number of different levels for that game which are enjoyable for humans to play [1]. This research investigates the characteristics of what makes automation of general level generation for 2D video games difficult, identifying what exactly makes it so challenging. Solutions such as algorithmic approaches and design patterns shall be presented. By investigating the techniques that have been used so far, empirical evidence will provide key insight into which techniques are most promising to improve level generation quality in the future.