David Churchill and Michael Buro
University of Alberta
Computing Science Department
Edmonton, Alberta, Canada, T6G 2E8
Real-time strategy (RTS) games are known to be one of the most complex game genres for humans to play, as well as one of the most difficult games for computer AI agents to play well. To tackle the task of applying AI to RTS games, recent techniques have focused on a divide-and-conquer approach, splitting the game into strategic components, and developing separate systems to solve each. This trend gives rise to a new problem: how to tie these systems together into a functional real-time strategy game playing agent. In this 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.
Churchill, D., and Buro, M. 2011. Build order optimization in StarCraft. In Proceedings of AIIDE.
Churchill, D.; Saffidine, A.; and Buro, M. Fast heuristic search for RTS game combat scenarios. In Proceedings of AIIDE, (pre-print available at www.cs.ualberta.ca/mburo/ps/aiide12-combat.pdf).
Furtak, T., and Buro, M. 2010. On the complexity of two-player attrition games played on graphs. In Youngblood, G. M., and Bulitko, V., eds., Proceedings of AIIDE.
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Wintermute, S.; Xu, J.; and Laird, J. E. 2007. Sorts: A human-level approach to real-time strategy ai. In Proceedings of the Third AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2007.