Blood Bowl: A New Board Game Challenge and Competition for AI

摘要

We propose the popular board game Blood Bowl as a new challenge for Artificial Intelligence (AI). Blood Bowl is a fully-observable, stochastic, turn-based, modern-style board game with a grid-based game board. At first sight, the game ought to be approachable by numerous game-playing algorithms. However, as all pieces on the board belonging to a player can be moved several times each turn, the turn-wise branching factor becomes overwhelming for traditional algorithms. Additionally, scoring points in the game is rare and difficult, which makes it hard to design heuristics for search algorithms or apply reinforcement learning. We present the Fantasy Football AI (FFAI) framework that implements the core rules of Blood Bowl and includes a forward model, several OpenAI Gym environments for reinforcement learning, competition functionalities, and a web application that allows for human play. We also present Bot Bowl I, the first AI competition that will use FFAI along with baseline agents and preliminary reinforcement learning results. Additionally, we present a wealth of opportunities for future AI competitions based on FFAI.