David Churchill and Michael Buro
University of Alberta
Computing Science Department
Edmonton, Alberta, Canada
In recent years, real-time strategy (RTS) games have gained interest in the AI research community for their multitude of challenging subproblems — such as collaborative pathfinding, effective resource allocation and unit targeting, to name a few. In this paper we consider the build order problem in RTS games in which we need to find concurrent action sequences that, constrained by unit dependencies and resource availability, create a certain number of units and structures in the shortest possible time span. We present abstractions and heuristics that speed up the search for approximative solutions considerably in the game of StarCraft, and show the efficacy of our method by comparing its real-time performance with that of professional StarCraft players.
Branquinho, A., and Lopes, C. 2010. Planning for resource production in real-time strategy games based on partial order planning, search and learning. In Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on
Buro, M., and Kovarsky, A. 2007. Concurrent action selection with shared fluents. In AAAI Vancouver, Canada
BWAPI. 2011. BWAPI: An API for interacting with StarCraft: Broodwar. http://code.google.com/p/bwapi/
Chan, H.; Fern, A.; Ray, S.; Wilson, N.; and Ventura, C. 2007a. Extending online planning for resource production in real-time strategy games with search.
Chan, H.; Fern, A.; Ray, S.; Wilson, N.; and Ventura, C. 2007b. Online planning for resource production in real-time strategy games. In Proceedings of the International Conference on Automated Planning and Scheduling, Providence, Rhode Island
Iba, G. 1989. A heuristic approach to the discovery of macro-operators. Machine Learning 3
Kovarsky, A., and Buro, M. 2006. A first look at buildorder optimization in real-time strategy games. In Proceedings of the GameOn Conference
, 18–22. Citeseer.
ORTS. 2010. ORTS - A Free Software RTS Game Engine. http://skatgame.net/mburo/orts/
Stolle, M., and Precup, D. 2002. Learning options in reinforcement learning. Abstraction, Reformulation, and Approximation