Planning in the midst of chaos: how a stochastic Blood Bowl model can help to identify key planning features

For several decades now, games have become an important research ground for artificial intelligence. In addition to often present useful and complex problems, they also provide a clear framework thanks to their rules, sometimes numerous. In this article, we explore a very difficult two-players board game named Blood Bowl. This game allows the players to perform many different actions, which depend for a large part on the result of one or more dice rolls. Thus, it can be seen as a multi-action probabilistic problem driven by a Markov decision process. In this article, we present the first stochastic model of the main phase of Blood Bowl to our knowledge and the premise of a dedicated planning framework. Such a framework could offer interesting grounds and insights for modeling high turn-wise branch factor games.