  Game tree search algorithms, such as Monte Carlo Tree Search (MCTS), require
access to a forward model (or "simulator") of the game at hand. However, in
some games such forward model is not readily available. This paper presents
three forward models for two-player attrition games, which we call "combat
models", and show how they can be used to simulate combat in RTS games. We also
show how these combat models can be learned from replay data. We use StarCraft
as our application domain. We report experiments comparing our combat models
predicting a combat output and their impact when used for tactical decisions
during a real game.
