Real-time strategy (RTS) games pose challenges to AI research on many levels, ranging from selecting targets in unit combat situations, over efficient multi-unit pathfinding, to high-level economic decisions. Due to the complexity of RTS games, writing competitive AI systems for these games requires high speed adaptive algorithms and simplified models of the game world. In this paper we focus on motion prediction and motion planning in StarCraft — a popular RTS game for which a C++ API exists that allows us to write AI systems to play the game. We explore our existing unit motion model of StarCraft and find and fix some inconsistencies to improve the model by accounting for systematic command execution delays and unit acceleration. We then investigate ways to improve existing combat motion planning systems that are based on discrete unit motion sets, and show that search-based algorithms and scripts can benefit from using a new direction set that considers moves towards the closest enemy unit, away from it, and perpendicular to both directions.

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. In this paper we address the problem of automatically learning forward models (more specifically, combats models) for two-player attrition games. We report experiments comparing several approaches to learn such combat model from replay data to models generated by hand. We use StarCraft, a Real-Time Strategy (RTS) game, as our application domain. Specifically, we use a large collection of already collected replays, and focus on learning a combat model for tactical combats.