Artificial Intelligence for the Card Game Durak
|Thesis title in Czech:||Umělá inteligence pro karetní hru Dudák|
|Thesis title in English:||Artificial Intelligence for the Card Game Durak|
|Key words:||artificial intelligence|card game|Durak|
|English key words:||artificial intelligence|card game|Durak|
|Academic year of topic announcement:||2021/2022|
|Type of assignment:||Bachelor's thesis|
|Department:||Department of Software and Computer Science Education (32-KSVI)|
|Supervisor:||Adam Dingle, M.Sc.|
|Author:||Azamat Zarlykov - assigned and confirmed by the Study Dept.|
|Date of registration:||14.04.2022|
|Date of assignment:||15.04.2022|
|Confirmed by Study dept. on:||28.04.2022|
|Date and time of defence:||07.02.2023 09:00|
|Date of electronic submission:||05.01.2023|
|Date of submission of printed version:||09.01.2023|
|Date of proceeded defence:||07.02.2023|
|Reviewers:||Tobias Rittig, B.Sc., M.Sc.|
|Durak (called Dudák in Czech) is a popular card game especially in Russian-speaking countries. Like many card games, it has elements of chance since cards are dealt randomly, and has hidden information since cards are not visible to all players. The game is interesting, but there has been little published research on algorithms for playing it. In this thesis work, the student will implement the game of Durak and will write agents that can play it using various techniques, including rules-based heuristics, minimax search, Monte Carlo tree search, and/or reinforcement learning. The student will compare and analyze the performance of these agents.|
|Bonnet, Édouard. "The complexity of playing Durak." 25th International Joint Conference on Artificial Intelligence (IJCAI 2016). 2016.
Niklaus, Joel, et al. "Survey of artificial intelligence for card games and its application to the Swiss game Jass." 2019 6th Swiss Conference on Data Science (SDS). IEEE, 2019.
Browne, Cameron B., et al. "A survey of Monte Carlo tree search methods." IEEE Transactions on Computational Intelligence and AI in games 4.1 (2012): 1-43.
Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press, 2018.