Pen and paper puzzles are a fun pastime to test your logical reasoning skills, with Sudoku being the most popular of these puzzles.
While the problem of solving these puzzles is usually in NP-Complete, generating them is more difficult, depending on the type of puzzle.
When generating puzzles for humans, we have to keep in mind how they solve them, as we cannot expect them to brute-force the solution.
Generation methods exist for simple number-based puzzles like the Sudoku, but these methods do not translate to the domain of language-based puzzles.
We take the concept of strategy-solving to mimic human solving methods and apply them to the Raatsel, a language-based puzzle, and the Sudoku, to generalize a common methodology for generating, solving and rating these problem instances.
A new method will be introduced to generate Raatsels using a reduction from Subgraph Isomorphism.
This concept of instance generation with specific properties has many applications outside of puzzles, such as validating correctness and generating training data for machine learning.

The focus of this project is to develop a web application that automates the process of drawing schematic networks from geographical networks. It allows users to upload geographical networks and inspect the schematic representation in the browser. During the two week research phase we found a Master's Thesis which explains a method for modelling railway tracks and junctions and attempts to draw schematics. We improve upon the findings of this thesis. We wrote a transformer that can transform real-world GeoJSON data of railway networks to abstract input usable by our algorithms. If our application is to be extended to other infrastructure networks, a different transformer can be implemented while using the same underlying algorithm. We performed weekly sprints. At the end of each, we presented the improvements to our client to receive feedback. With this feedback we created a sprint plan to assign and prioritise the tasks and responsibilities of the next sprint. The testing of our application is based on extensive unit tests and end-to-end tests. We evaluated the results of our application and documented recommendations for improving the algorithm. Our application serves as a proof-of-concept to our client.