Blood Bowl: A New Board Game Challenge and Competition for AI
Blood Bowl: A New Board Game Challenge and Competition for AI
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Blood Bowl：人工智能的新棋盘游戏挑战和竞赛
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
S. Risi
中科院分区：
文献类型：
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作者：
Niels Justesen;Lasse Møller Uth;Christopher Jakobsen;Peter David Moore;J. Togelius;S. Risi
我们提出了流行的棋盘游戏血碗作为人工智能(AI)的新挑战。血碗是一款完全可观察的、随机的、以回合为基础的现代风格的棋盘游戏，带有基于网格的棋盘。乍一看，这个游戏应该是通过许多游戏算法来接近的。然而，由于棋盘上属于一名玩家的所有棋子都可以在每个回合中移动几次，因此回合方向的分支因素对传统算法来说变得不堪重负。此外，游戏中的得分点很少，也很困难，这使得为搜索算法设计启发式算法或应用强化学习变得困难。我们提出了梦幻足球AI(FFAI)框架，它实现了血碗的核心规则，包括一个向前模型、几个用于强化学习的OpenAI健身房环境、比赛功能和一个允许人类游戏的Web应用程序。我们还介绍了Bot Bowl I，这是第一个使用FFAI的人工智能比赛，以及基线代理和初步强化学习结果。此外，我们还为基于FFAI的未来人工智能比赛提供了丰富的机会。
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.
DOI：
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发表时间：
2018-06
期刊：
arXiv: Learning
影响因子：
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作者：
Niels Justesen;R. Torrado;Philip Bontrager;A. Khalifa;J. Togelius;S. Risi
通讯作者：
Niels Justesen;R. Torrado;Philip Bontrager;A. Khalifa;J. Togelius;S. Risi