Bachelor/Master Thesis: Rule Reduction for Complex Systems like Programming Languages


In the pursuit of unraveling complex systems, there arises a need to simplify the intricate web of rules that often obscures understanding. The task of memorizing countless rules can be tiresome and time-consuming, yet it is often that only a fraction of these rules are essential for a basic grasp of the system. By distilling these core principles, we can make these systems more accessible, optimizing the learning experience and paving the way for a deeper understanding.
Leveraging machine learning techniques to rank and prioritize these rules based on their importance, we can streamline the learning process and maximize knowledge acquisition within the constraints of a limited number of rules. Furthermore, delving into the hierarchy of rules not only enhances comprehension but also might unveil the underlying structure and logic that governs the system.


  1. Find a good rule-based system, e.g. a programming language (like JavaScript) and get familiar with the utilization
  2. Create a machine learning pipeline for ranking rules and evaluating the results
  3. Compare different methods (the amount depends on Master or Bachelor Thesis)


  • Strong Programming skills (preferable Python, knowledge in JavaScript is also a plus)
  • The ability to quickly pick up new methods
  • At least basic knowledge in machine learning

If you like to have a very interesting topic or just have questions about the topic in any way, please reach out to Alexander Studt ( and I can give a more detailed overview of the thesis topic.
If you have your own ideas, which are somewhat similar, you can also pitch it to me and maybe we will find a promising topic together.