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

Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms

We investigate the impact of supervised prediction models on the strength and efficiency of artificial agents that use the Monte-Carlo Tree Search (MCTS) algorithm to play a popular video game Hearthstone: Heroes of Warcraft. We overview our custom implementation of the MCTS that is well-suited for games with partially hidden information and random effects. We also describe experiments which we designed to quantify the performance of our Hearthstone agent's decision making. We show that even simple neural networks can be trained and successfully used for the evaluation of game states. Moreover, we demonstrate that by providing a guidance to the game state search heuristic, it is possible to substantially improve the win rate, and at the same time reduce the required computations.Comment: Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games (CIG'18); pages 445-452; ISBN: 978-1-5386-4358-

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

Multiplatform Card Game with Artificial Intelligence

Tato práce se zabývá umělou inteligencí v karetních hrách. Cílem je implementovat multiplatformní hru tohoto žánru v herním enginu Unity, shrnout možné přístupy vytváření inteligentních protihráčů a pro zvolenou hru navrhnout a popsat metodu nejvhodnější, případně kombinaci několika. Provedený výzkum ukázal, že problémová doména je u karetních her většinou dosti specifická a to znesnadňuje užití univerzálních algoritmů. Zvolený problém je vyřešen formou rule-based umělé inteligence. Podařilo se vytvořit inteligentního hráče pro zástupce z kategorie imperfect information games, což je jeden z hlavních přínosů této práce. Ačkoli se dopouští drobných taktických prohřešků, jeho chování většinou blízce připomíná smýšlení středně zkušených hráčů.This thesis focuses on artificial intelligence in card games. The goal is to implement a multi-platform game of this genre in the Unity game engine, to summarize possible approaches that are being used in order to create intelligent agents and furthermore to design and describe the most suitable method or combination of methods for the chosen game. The research that was carried out has shown that the problem domain of card games is rather specific, making it more difficult to use the general-purpose algorithms. The problem given was solved using the rule-based artificial intelligence. The intelligent agent has been implemented for a game of imperfect information, which is considered to be the main contribution of this work to the community. Even though the artificial intelligence player is making minor tactical mistakes, his behavior closely resembles the way of thinking of semi-experienced players.