Blood Bowl: A New Board Game Challenge and Competition for AI Niels Justesen Lasse Møller Uth Christopher Jakobsen IT University of Copenhagen IT University of Copenhagen IT University of Copenhagen Copenhagen, Denmark Copenhagen, Denmark Copenhagen, Denmark
[email protected] [email protected] [email protected] Peter David Moore Julian Togelius Sebastian Risi Sydney, Australia New York University IT University of Copenhagen
[email protected] New York, USA Copenhagen, Denmark
[email protected] [email protected] Abstract—We propose the popular board game Blood Bowl Learning Environment (SC2LE) [19]. This paper additionally as a new challenge for Artificial Intelligence (AI). Blood Bowl presents the Fantasy Football AI (FFAI) framework, a Python is a fully-observable, stochastic, turn-based, modern-style board implementation of Blood Bowl with an API for scripted bots game with a grid-based game board. At first sight, the game ought to be approachable by numerous game-playing algorithms. and several OpenAI Gym environments, including scaled down However, as all pieces on the board belonging to a player can be versions of Blood Bowl. We also describe a scripted Blood moved several times each turn, the turn-wise branching factor Bowl bot called GrodBot and present preliminary results of becomes overwhelming for traditional algorithms. Additionally, training a deep reinforcement learning agent in three smaller scoring points in the game is rare and difficult, which makes it variants of Blood Bowl. Additionally, this paper details plans hard to design heuristics for search algorithms or apply rein- forcement learning. We present the Fantasy Football AI (FFAI) for several future AI competitions using FFAI.