4 research outputs found
Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone
We present the design of a competitive artificial intelligence for Scopone, a
popular Italian card game. We compare rule-based players using the most
established strategies (one for beginners and two for advanced players) against
players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo
Tree Search (ISMCTS) with different reward functions and simulation strategies.
MCTS requires complete information about the game state and thus implements a
cheating player while ISMCTS can deal with incomplete information and thus
implements a fair player. 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.Comment: Preprint. Accepted for publication in the IEEE Transaction on Game
Developing Artificial Intelligence Agents for a Turn-Based Imperfect Information Game
Artificial intelligence (AI) is often employed to play games, whether to entertain human opponents, devise and test strategies, or obtain other analytical data. Games with hidden information require specific approaches by the player. As a result, the AI must be equipped with methods of operating without certain important pieces of information while being aware of the resulting potential dangers. The computer game GNaT was designed as a testbed for AI strategies dealing specifically with imperfect information. Its development and functionality are described, and the results of testing several strategies through AI agents are discussed
Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass
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