In this paper, we explore and compare multiple algorithms for solving the complex strategy game of Terra Mystica, hereafter abbreviated as TM. Previous work in the area of super-human game-play using AI has proven effective, with recent break-through for generic algorithms in games such as Go, Chess, and Shogi \cite{AlphaZero}. We directly apply these breakthroughs to a novel state-representation of TM with the goal of creating an AI that will rival human players. Specifically, we present the initial results of applying AlphaZero to this state-representation and analyze the strategies developed. A brief analysis is presented. We call this modified algorithm with our novel state-representation AlphaTM. In the end, we discuss the success and shortcomings of this method by comparing against multiple baselines and typical human scores. All code used for this paper is available at on \href{https://github.com/kandluis/terrazero}{GitHub}.

中文翻译：

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掌握Terra Mystica：将自玩游戏应用于多代理合作棋盘游戏

在本文中，我们探索并比较了用于解决Terra Mystica（以下简称TM）的复杂策略博弈的多种算法。事实证明，以前使用AI进行超人游戏的工作是有效的，最近在Go，Chess和Shogi \ cite {AlphaZero}等游戏中突破了通用算法的突破。我们直接将这些突破应用于TM的一种新颖的状态表示形式，目的是创建一种可以与人类玩家匹敌的AI。具体来说，我们介绍了将AlphaZero应用于此状态表示的初步结果，并分析了开发的策略。简要分析。我们将这种改进的算法与我们新颖的状态表示AlphaTM一起使用。最后，我们通过与多个基线和典型的人类得分进行比较，讨论了该方法的成功与不足。