  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}.
