Challenging human supremacy: Evaluating monte carlo tree search and deep learning for the trick taking card game jass

Niklaus, Joël; Alberti, Michele; Ingold, Rolf; Stolze, Markus; Koller, Thomas (12 October 2020). Challenging human supremacy: Evaluating monte carlo tree search and deep learning for the trick taking card game jass In: International Conference on Artificial Intelligence and Soft Computing ICAISC 2020. Lecture Notes in Computer Science: Vol. 12416 (pp. 505-517). Springer 10.1007/978-3-030-61534-5_45

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Despite the recent successful application of Artificial Intelligence (AI) to games, the performance of cooperative agents in imperfect information games is still far from surpassing humans. Cooperating with teammates whose play-styles are not previously known poses additional challenges to current state-of-the-art algorithms. In the Swiss card game Jass, coordination within the two opposing teams is crucial for winning. Since verbal communication is forbidden, the only way to transmit information within the team is through a player’s play-style. This makes the game a particularly suitable candidate subject to continue the research on AI in cooperation games with hidden information. In this work, we analyse the effectiveness and shortcomings of several state of-the-art algorithms (Monte Carlo Tree Search (MCTS) variants and Deep Neural Networks (DNNs)) at playing the Jass game. Our key contributions are two-fold …

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	Business School > Institute for Public Sector Transformation > Data and Infrastructure Business School
Name:	Niklaus, Joël0000-0002-2779-1653; Alberti, Michele; Ingold, Rolf; Stolze, Markus and Koller, Thomas
ISBN:	978-3-030-61533-8
Series:	Lecture Notes in Computer Science
Publisher:	Springer
Language:	English
Submitter:	Joël Niklaus
Date Deposited:	25 Aug 2023 11:10
Last Modified:	06 Dec 2023 12:05
Publisher DOI:	10.1007/978-3-030-61534-5_45
Related URLs:	Organisation Organisation
ARBOR DOI:	10.24451/arbor.19711
URI:	https://arbor.bfh.ch/id/eprint/19711