A Skat Player Based on Monte-Carlo Simulation

Kupferschmid, Sebastian; Helmert, Malte

doi:10.1007/978-3-540-75538-8_12

Cited by 22 publications

(13 citation statements)

References 8 publications

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“…For the same game, Ginsberg (2001) has applied perfect-information Monte Carlo sampling. Similar special case applications of sampling to reduce imperfect to perfect information can be found in Kupferschmid and Helmert (2007). The Alberta Computer Poker Research Group has developed systems at the forefront of computer Poker players (Billings et al, 2006).…”

Section: Related Researchmentioning

confidence: 86%

General Game Playing with Imperfect Information

Schofield¹,

Thielscher²

2019

jair

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General Game Playing is a field which allows the researcher to investigate techniques that might eventually be used in an agent capable of Artificial General Intelligence. Game playing presents a controlled environment in which to evaluate AI techniques, and so we have seen an increase in interest in this field of research. Games of imperfect information offer the researcher an additional challenge in terms of complexity over games with perfect information. In this article, we look at imperfect-information games: their expression, their complexity, and the additional demands of their players. We consider the problems of working with imperfect information and introduce a technique called HyperPlay, for efficiently sampling very large information sets, and present a formalism together with pseudo code so that others may implement it. We examine the design choices for the technique, show its soundness and completeness then provide some experimental results and demonstrate the use of the technique in a variety of imperfect-information games, revealing its strengths, weaknesses, and its efficiency against randomly generating samples. Improving the technique, we present HyperPlay-II, capable of correctly valuing information-gathering moves. Again, we provide some experimental results and demonstrate the use of the new technique revealing its strengths, weaknesses and its limitations.

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Section: Related Researchmentioning

confidence: 86%

General Game Playing with Imperfect Information

Schofield¹,

Thielscher²

2019

jair

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“…Mitsukami et al [28] developed a par-human AI for Japanese Mahjong using MC Simulation. Kupferschmid et al [29] applied MC Simulation to Skat to obtain the gametheoretical value of a Skat hand. Note that they converted the game to a PIG by making all the cards known.…”

Section: A Monte Carlo Simulationmentioning

confidence: 99%

“…Sievers et al [33] applied UCT to Doppelkopf reaching par-human performance. Schäfer [34] used UCT to build an AI for Skat, which is still sub-human but comparable to the MC Simulation based player proposed by Kupferschmid et al [29]. Swiechowski et al [35] combined an MCTS player with supervised learning on the logs of sample games, achieving par-human performance.…”

Section: Monte Carlo Tree Searchmentioning

confidence: 99%

Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass

Niklaus

Alberti

Pondenkandath

et al. 2019

2019 6th Swiss Conference on Data Science (SDS)

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In the last decades we have witnessed the success of applications of Artificial Intelligence to playing games. In this work we address the challenging field of games with hidden information and card games in particular. Jass is a very popular card game in Switzerland and is closely connected with Swiss culture. To the best of our knowledge, performances of Artificial Intelligence agents in the game of Jass do not outperform top players yet. Our contribution to the community is two-fold. First, we provide an overview of the current state-of-the-art of Artificial Intelligence methods for card games in general. Second, we discuss their application to the use-case of the Swiss card game Jass. This paper aims to be an entry point for both seasoned researchers and new practitioners who want to join in the Jass challenge.

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“…We present in this paper the memory reduction that can be expected from using this property to compute complete information endgame tables. Perfect information endgame tables are important in skat since the Monte Carlo approach in Skat consists in solving many perfect information versions of the current hand [11,1,13]. Using endgame tables enables to speedup the solver.…”

Section: Introductionmentioning

confidence: 99%

Material Symmetry to Partition Endgame Tables

Saffidine

Jouandeau

Buron

et al. 2014

Computers and Games

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Abstract. Many games display some kind of material symmetry. That is, some sets of game elements can be exchanged for another set of game elements, so that the resulting position will be equivalent to the original one, no matter how the elements were arranged on the board. Material symmetry is routinely used in card game engines when they normalize their internal representation of the cards.Other games such as chinese dark chess also feature some form of material symmetry, but it is much less clear what the normal form of a position should be. We propose a principled approach to detect material symmetry. Our approach is generic and is based on solving multiple relatively small sub-graph isomorphism problems. We show how it can be applied to chinese dark chess, dominoes, and skat.In the latter case, the mappings we obtain are equivalent to the ones resulting from the standard normalization process. In the two former cases, we show that the material symmetry allows for impressive savings in memory requirements when building endgame tables. We also show that those savings are relatively independent of the representation of the tables.

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A Skat Player Based on Monte-Carlo Simulation

Cited by 22 publications

References 8 publications

General Game Playing with Imperfect Information

General Game Playing with Imperfect Information

Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass

Material Symmetry to Partition Endgame Tables

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