Extracting Rules for Black Jack Using Machine Learning and Fuzzy Systems

Black Jack is a card game played by two or more players who aim at reaching 21 points. It is one of the most played card games as it presents an advantage for the player against the Casino: the player decides what to do before the dealer and he/she may stand (stop getting cards) when reaching a sum close to and smaller than 21. In the literature, it is possible to find some techniques proposed to support the prediction of the decision to be taken in accordance to the player's hand. Such techniques focus on finding a way to minimize the casino's advantage and turn the odds in favour of the player. Such strategies currently used were defined in the 1960's by mathematicians based on probability with hundreds of hands. However, these strategies are complex, making it difficult to memorize all possible actions to be taken. In this sense, this study aims to analyse data sets containing information on Black Jack hands in order to obtain rules to favour the player. This is an initial work, as there is a lack of proposals in the literature, based on the hypothesis that we could obtain sets of rules that could be used to support the player's decisions using machine learning algorithms. We used two fuzzy rule-based algorithms, namely FuzzyDT and FuzzyFCA, as well as the classic C4.5, PART, and Ripper algorithms to extract rules. The rules obtained are described and the results discussed.