Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone

@article{Palma2018TraditionalWA,
  title={Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone},
  author={Stefano Di Palma and Pier Luca Lanzi},
  journal={IEEE Transactions on Games},
  year={2018},
  volume={10},
  pages={317-332}
}
  • S. Palma, P. Lanzi
  • Published 9 May 2018
  • Computer Science
  • IEEE Transactions on Games
We present the design of a competitive artificial intelligence for Scopone, a popular Italian card game. [] Key Result Our results show that, as expected, the cheating MCTS outperforms all the other strategies; ISMCTS is stronger than all the rule-based players implementing well-known and most advanced strategies and it also turns out to be a challenging opponent for human players.
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8 Citations
Highly Influential Citations
1
Background Citations
1
Methods Citations
3

8 Citations

Challenging Human Supremacy: Evaluating Monte Carlo Tree Search and Deep Learning for the Trick Taking Card Game Jass
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This work analyses the effectiveness and shortcomings of several state-of-the-art algorithms at playing the Jass game, and implements and open-source a platform where different agents can play Jass in an automated fashion, effectively reducing the overhead for other researchers who want to perform further experiments.
Monte Carlo Tree Search: A Review of Recent Modifications and Applications
TLDR
In more complex games (e.g. those with a high branching factor or real-time ones), an efficient MCTS application often requires its problem-dependent modification or integration with other techniques and domain-specific modifications and hybrid approaches are the main focus of this survey.
An algorithm based on valuation forecasting for game tree search
TLDR
Improved VIS has a better performance in solving game tree with less nodes generated and expanded than original VIS and is superior to α–β search, PNS and MCTS algorithm in solving the game tree and finding the best move for some game scenarios by consuming less time and few memory resources.
Evaluating the Complexity of Players’ Strategies using MCTS Iterations
  • P. Lanzi
  • Computer Science
    2019 IEEE Conference on Games (CoG)
  • 2019
TLDR
This paper proposes to use MCTS to qualitatively evaluate the strength of artificial players as the minimum number of iterations that M CTS needs to perform equivalently to the target player, and introduces a bisection procedure to compute the MCTs complexity of a player.
Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass
TLDR
This work provides an overview of the current state-of-the-art of Artificial Intelligence methods for card games in general and their application to the use-case of the Swiss card game Jass.
A Rule-Based AI Method for an Agent Playing Big Two
TLDR
The experimental results show that the proposed rule-based AI agent can play Big Two well and outperform randomized AI, conventional AI, and human players, with the capability of maximizing the winning score and minimizing the number of cards left when the chance of winning is low.
A Method for Geodesic Distance on Subdivision of Trees With Arbitrary Orders and Their Applications
TLDR
The results suggest that the two topological operations are sharply different from each other, particularly, $MFPT$ for random walks, and however have likely to show the same function, at least, on average geodesic distance.
Developing Artificial Intelligence Agents for a Turn-Based Imperfect Information Game

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