Dominion: A New Frontier for AI Research (2405.06846v1)
Abstract: In recent years, machine learning approaches have made dramatic advances, reaching superhuman performance in Go, Atari, and poker variants. These games, and others before them, have served not only as a testbed but have also helped to push the boundaries of AI research. Continuing this tradition, we examine the tabletop game Dominion and discuss the properties that make it well-suited to serve as a benchmark for the next generation of reinforcement learning (RL) algorithms. We also present the Dominion Online Dataset, a collection of over 2,000,000 games of Dominion played by experienced players on the Dominion Online webserver. Finally, we introduce an RL baseline bot that uses existing techniques to beat common heuristic-based bots, and shows competitive performance against the previously strongest bot, Provincial.
- D. X. Vaccarino. Dominion, 2008. URL http://riograndegames.com/Game/278-Dominion.
- Dominion strategy wiki. URL http://wiki.dominionstrategy.com/.
- S. Meijer. Dominion online, 2017. URL dominion.games.
- M. E. Glickman. Example of the glicko-2 system. Technical report, Boston University, 11 2013. unpublished.
- S. Meijer. Rating details, 3 2017. URL http://forum.shuffleit.nl/index.php?topic=1679.msg5891.
- M. D. Fisher. Provincial: A kingdom-adaptive ai for dominion. Technical report, Stanford, 2014. unpublished.
- Rainbow: Combining improvements in deep reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.
- R. R. van der Heijden. An analysis of dominion. Master’s thesis, Leiden University, 8 2014.
- Developing an agent for dominion using modern ai-approaches. Master’s thesis, IT-University of Copenhagen, 2010.
- Evolving card sets towards balancing dominion. In 2012 IEEE Congress on Evolutionary Computation, pages 1–8. IEEE, 2012.
- J. V. Jansen and R. Tollisen. An AI for Dominion Based on Monte-Carlo Methods. PhD thesis, University of Agder, 6 2014.
- R. K. Winder. Methods for approximating value functions for the dominion card game. Evolutionary Intelligence, 6(4):195–204, 2014.
- C. Fenner. Ann assisted heuristic tree search in computer dominion. Master’s thesis, University of Oklahoma, 2014.