  StarCraft, one of the most popular real-time strategy games, is a compelling
environment for artificial intelligence research for both micro-level unit
control and macro-level strategic decision making. In this study, we address an
eminent problem concerning macro-level decision making, known as the
'fog-of-war', which rises naturally from the fact that information regarding
the opponent's state is always provided in the incomplete form. For intelligent
agents to play like human players, it is obvious that making accurate
predictions of the opponent's status under incomplete information will increase
its chance of winning. To reflect this fact, we propose a convolutional
encoder-decoder architecture that predicts potential counts and locations of
the opponent's units based on only partially visible and noisy information. To
evaluate the performance of our proposed method, we train an additional
classifier on the encoder-decoder output to predict the game outcome (win or
lose). Finally, we designed an agent incorporating the proposed method and
conducted simulation games against rule-based agents to demonstrate both
effectiveness and practicality. All experiments were conducted on actual game
replay data acquired from professional players.
