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 agricultural industry is undergoing a transformative shift towards efficiency and large-scale production. Manual labor is unable to meet the increasing demand, leading to the development of automation techniques. During production, objects are adjusted and moved along a line to maintain productivity. This work investigates the feasibility of developing a system that detects and determines the position and rotation of a small clipping object in a cluttered environment. To achieve this feasibility, a neural network has been trained on a custom dataset with images containing 6-D pose annotations of a clip that do not require manual annotations. The designed system also executes a validation step with a second set of sensors in the form of a stereo camera. The entire methodology has been evaluated on a test setup. The small and round properties of the clip cause a rotational error that does not fall in positional and rotational requirements set by a mechanical grabber on a robotic arm. Higher positional precision of annotation data and depth information from the stereo camera is necessary to make this methodology feasible.

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.