Incorporating Search Algorithms into RTS Game Agents

Real-time strategy (RTS) games are known to be one of the most complex gamegenres for humans to play, as well as one of the most difficult games forcomputer AI agents to play well. To tackle the task of applying AI to RTSgames, recent techniques have focused on a divide-and-conquer approach,splitting the game into strategic components, and developing separate systemsto solve each. This trend gives rise to a new problem: how to tie thesesystems together into a functional real-time strategy game playing agent. Inthis paper we discuss the architecture of UAlbertaBot, our entry into the 2011/2012 StarCraft AI competitions, and the techniques used to include heuristic search based AI systems for the intelligent automation of both build order planning and unit control for combat scenarios.