Master Thesis: Development of an Adaptive AI for Cooperative Problem-Solving in the Context of the Card Game Bridge (still open!)


This master’s thesis proposal is dedicated to the creation of an advanced artificial intelligence (AI) system specifically tailored for collaborative problem-solving, a critical aspect of Human-Computer Interaction (HCI). Within the HCI framework, the card game Bridge serves as an exemplary use case, providing a complex, strategic environment where AI and human cognition can intersect and interact.

The objective is to harness the intricacies of Bridge – a game that inherently requires partnership and cooperation – to develop an AI that not only understands the rules and strategies of the game but can also adapt its play style in response to the behavior and preferences of its human partner. Such adaptability goes beyond mere reactive play; it entails an AI that can predict, learn, and synergize with a human’s unique approach to the game.


  1. Development of an API for the Bridge game to establish an experimental foundation.
  2. pplication and testing of state-of-the-art reinforcement learning methods to develop an AI that collaborates with human players.
  3. The AI should not solely aim for optimal decisions but should particularly adapt to the skill level or preferences of the human partner.


  • Good Knowledge in Machine Learning
  • Knowledge in Reinforcement learning would be even better
  • Good Python skills with experience in relevant libraries (e.g. TensorFlow or PyTorch)

Be aware that this is a time demanding topic. If you like to have a challenging but also very interesting topic please reach out to Alexander Studt ( and I can give a more detailed overview of the thesis topic.