  Real-time strategy (RTS) games make heavy use of artificial intelligence
(AI), especially in the design of computerized opponents. Because of the
computational complexity involved in managing all aspects of these games, many
AI opponents are designed to optimize only a few areas of playing style. In
games like StarCraft 2, a very popular and recently released RTS, most AI
strategies revolve around economic and building efficiency: AI opponents try to
gather and spend all resources as quickly and effectively as possible while
ensuring that no units are idle. The aim of this work was to help address the
need for AI combat strategies that are not computationally intensive. Our goal
was to produce a computationally efficient model that is accurate at predicting
the results of complex battles between diverse armies, including which army
will win and how many units will remain. Our results suggest it may be possible
to develop a relatively simple approximation model of combat that can
accurately predict many battles that do not involve micromanagement. Future
designs of AI opponents may be able to incorporate such an approximation model
into their decision and planning systems to provide a challenge that is
strategically balanced across all aspects of play.
