Reinforcement learning for board games

Sebastian Vollmer
Last updated on 15 Nov 2022

This project focuses on learning strategies that help humans to improve their performance. Additionally, we will consider the problem of detecting non-human players. An initial focus will be the card game 6 nimmt!.

  • Can we translate strategies learnt by machines into rules simple enough to be understood and used by human players?
  • Can we distinguish ‘real’ players from ‘bots’ based on their play style?

Further details are available on request.

References

  • R. Bettker, P. Minini, G. Pereira and J. V. C. Assunção, “Towards playing AIs for 7 Wonders: main patterns and strategies for 3-player games”, 2021 20th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), 2021, pp. 172-181, doi: 10.1109/SBGames54170.2021.00029.
reinforcement learning
Sebastian Vollmer
Sebastian Vollmer
Professor for Applications of Machine Learning

My research interests lie at the interface of applied probability, statistical inference and machine learning.