  We have seen numerous machine learning methods tackle the game of chess over
the years. However, one common element in these works is the necessity of a
finely optimized look ahead algorithm. The particular interest of this research
lies with creating a chess engine that is highly capable, but restricted in its
look ahead depth. We train a deep neural network to serve as a static
evaluation function, which is accompanied by a relatively simple look ahead
algorithm. We show that our static evaluation function has encoded some
semblance of look ahead knowledge, and is comparable to classical evaluation
functions. The strength of our chess engine is assessed by comparing its
proposed moves against those proposed by Stockfish. We show that, despite
strict restrictions on look ahead depth, our engine recommends moves of equal
strength in roughly $83\%$ of our sample positions.
