Abstract
During recent years, AI found its ways to games. Here, imperfect information games, such as Thousand Schnapsen, bring about major challenges. In this work, the rules and characteristics of this game have been described. Next, an overview of existing literature, focusing on similar problems, has been presented, followed by a summary of selected methods for finding an optimal strategy along with applied modifications. Finally, the results of the experiments are discussed.
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References
Bergh MVD, Hommelberg A, Kosters W, Spieksma F (2016) Aspects of the cooperative card game hanabi. In: BNCAI
Brown N, Lerer A, Gross S, Sandholm T (2019) Deep counterfactual regret minimization. In: Proceedings of the 36th international conference on machine learning, pp 793–802
Heinrich J, Silver D (2016) Deep reinforcement learning from self-play in imperfect-information games (03)
Niklaus J, Alberti M, Pondenkandath V, Ingold R, Liwicki M (2019) Survey of artificial intelligence for card games and its application to the SWISS game JASS (06)
Osawa H (2015) Solving hanabi: estimating hands by opponent’s actions in cooperative game with incomplete information. In: Ganzfried S (ed) Computer poker and imperfect information, papers from the 2015 AAAI workshop, Austin, Texas, USA. 26 Jan 2015
Risk N, Szafron D (2010) Using counterfactual regret minimization to create competitive multiplayer poker agents (01), 159–166
Robilliard D, Fonlupt C, Teytaud F (2014) Monte-Carlo tree search for the game of “7 wonders” (08), 64–77
Sutton RS, Barto AG (2018) Reinforcement learning: an introduction. The MIT Press, 2nd edn. http://incompleteideas.net/book/the-book-2nd.html
Ward C, Cowling P (2009) Monte Carlo search applied to card selection in magic: the gathering (10), 9 – 16. https://doi.org/10.1109/CIG.2009.5286501
Wisser F (2010) Creating possible worlds using sims tables for the imperfect information card game schnapsen 2(11), 7–10. https://doi.org/10.1109/ICTAI.2010.76
Wisser F (2015) An expert-level card playing agent based on a variant of perfect information monte carlo sampling. In: Proceedings of the 24th international conference on artificial intelligence, pp 125–131. IJCAI’15, AAAI Press
Zinkevich M, Johanson M, Bowling M, Piccione C (2007) Regret minimization in games with incomplete information. In: Proceedings of the 20th international conference on neural information processing systems, pp 1729–1736. NIPS’07, Curran Associates Inc., Red Hook, NY, USA
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Domańska, A., Ganzha, M., Paprzycki, M. (2022). Teaching Bot to Play Thousand Schnapsen. In: Tomar, A., Malik, H., Kumar, P., Iqbal, A. (eds) Machine Learning, Advances in Computing, Renewable Energy and Communication. Lecture Notes in Electrical Engineering, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-16-2354-7_57
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DOI: https://doi.org/10.1007/978-981-16-2354-7_57
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