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

Smart Blackjack : Aplicación de técnicas de aprendizaje para soporte en juegos de azar

El empleo de aprendizaje computacional como soporte a los juegos de azar plantea diferentes retos. Uno de ellos es poder sobreponerse a la aleatoriedad de ciertas circunstancias de las partidas. Para ello, es necesario definir adecuadamente las características que se van a usar en el aprendizaje y seleccionar cuidadosamente el método de clasificación. Este proyecto presenta un primer sistema de soporte en las partidas de Blackjack, donde el jugador se enfrenta al crupier cuyas cartas son desconocidas. Para desarrollar el proyecto, se han seleccionado 5 algoritmos de aprendizaje computacional y se han definido features basadas en el conocimiento de la partida actual y de la baraja de juego. Con el fin de poder testar el sistema desarrollado, se ha diseñado una interfaz gráfica de usuario que permite asimismo guardar las partidas jugadas para crear así una base de datos de pruebas. Se han comparado los resultados obtenidos por los métodos computacionales con los proporcionados por estrategias de juego que representan el comportamiento humano. Los resultados muestran que el uso de sistemas de soporte conlleva un incremento en el número de partidas ganadas, siendo éste incremento mayor para el algoritmo kNN.The use of machine learning to support to gambling poses different challenges. One of them is that support systems should be able to features the randomness of certain circumstances of the games. To cope with this, it is necessary to define adequately the characteristics that are going to be used in learning stage and to carefully select the classification method. This project introduces a support system to support player in Blackjack game. In this game, the player competes against the crupier, whose cards are unknown. To develop the project, five different machine learning algorithms have been selected and features based on the knowledge of the current status of the game and the game deck have been defined. A graphical user interface has been designed to test the system. This interface lso allows saving the games already played to create a test database. We have compared the results obtained by computational methods with those provided by game strategies that represent human behavior. The results show that the use of support systems entails an increase in the number of games won, this increase being greater for the kNN algorithm.L'ocupació d'aprenentatge computacional com a suport als jocs d'atzar planteja diferents reptes. Un d'ells és poder sobreposar-se a l'aleatorietat de certes circumstàncies de les partides. Per a això, cal definir adequadament les característiques que es van a usar en l'aprenentatge i seleccionar acuradament el mètode de classificació. Aquest projecte presenta un primer sistema de suport a les partides de BlackJack, on el jugador s'enfronta al crupier que té cartes desconegudes.. Per desenvolupar el projecte, s'han seleccionat 5 algoritmes d'aprenentatge computacional i s'han definit features basades en el coneixement de la partida actual i de la baralla de joc. Per tal de poder testar el sistema desenvolupat, s'ha dissenyat una interfície gràfica d'usuari que permet així mateix guardar les partides jugades per crear així una base de dades de proves. S'han comparat els resultats obtinguts pels mètodes computacionals amb els proporcionats per estratègies de joc que representen el comportament humà. Els resultats mostren que l'ús de sistemes de suport comporta un increment en el nombre de partides guanyades, sent aquest increment més gran per l'algoritme kNN

Merge-and-shrink abstractions for classical planning : theory, strategies, and implementation

Classical planning is the problem of finding a sequence of deterministic actions in a state space that lead from an initial state to a state satisfying some goal condition. The dominant approach to optimally solve planning tasks is heuristic search, in particular A* search combined with an admissible heuristic. While there exist many different admissible heuristics, we focus on abstraction heuristics in this thesis, and in particular, on the well-established merge-and-shrink heuristics. Our main theoretical contribution is to provide a comprehensive description of the merge-and-shrink framework in terms of transformations of transition systems. Unlike previous accounts, our description is fully compositional, i.e. can be understood by understanding each transformation in isolation. In particular, in addition to the name-giving merge and shrink transformations, we also describe pruning and label reduction as such transformations. The latter is based on generalized label reduction, a new theory that removes all of the restrictions of the previous definition of label reduction. We study the four types of transformations in terms of desirable formal properties and explain how these properties transfer to heuristics being admissible and consistent or even perfect. We also describe an optimized implementation of the merge-and-shrink framework that substantially improves the efficiency compared to previous implementations. Furthermore, we investigate the expressive power of merge-and-shrink abstractions by analyzing factored mappings, the data structure they use for representing functions. In particular, we show that there exist certain families of functions that can be compactly represented by so-called non-linear factored mappings but not by linear ones. On the practical side, we contribute several non-linear merge strategies to the merge-and-shrink toolbox. In particular, we adapt a merge strategy from model checking to planning, provide a framework to enhance existing merge strategies based on symmetries, devise a simple score-based merge strategy that minimizes the maximum size of transition systems of the merge-and-shrink computation, and describe another framework to enhance merge strategies based on an analysis of causal dependencies of the planning task. In a large experimental study, we show the evolution of the performance of merge-and-shrink heuristics on planning benchmarks. Starting with the state of the art before the contributions of this thesis, we subsequently evaluate all of our techniques and show that state-of-the-art non-linear merge-and-shrink heuristics improve significantly over the previous state of the art

A Doppelkopf Player Based on UCT

We propose doppelkopf, a trick-taking card game with similarities to skat, as a benchmark problem for AI research. While skat has been extensively studied by the AI community in recent years, this is not true for doppelkopf. However, it has a substantially larger state space than skat and a unique key feature which distinguishes it from skat and other card games: players usually do not know with whom they play at the start of a game, figuring out the parties only in the process of playing. Since its introduction in 2006, the UCT algorithm has been the dominating approach for solving games in AI research. It has notably achieved a playing strength comparable to good human players at playing go, but it has also shown good performance in card games like Klondike solitaire and skat. In this work, we adapt UCT to play doppelkopf and present an algorithm that generates random card assignments, used by the UCT algorithm for sampling. In our experiments, we discuss and evaluate different variants of the UCT algorithm, and we show that players based on UCT improve over simple baseline players and exhibit good card play behavior also when competing with a human player