Real-time strategy (RTS) games represent a mainstream genre of video games. They are also practical test-beds for intelligent agents, which have received considerable interest from Artificial Intelligence (AI) researchers, in particular game AI researchers. Terrain knowledge understanding is a fundamental issue for RTS agents and map decomposition methods can help AI agents in representing terrain knowledge. These contributions support AI agents’ path finding and combat strategy. In some RTS games, such as StarCraft, all terrain information is provided to AI agents at the beginning of the game. This presents an unfair advantage, as human players do not have access to this information. Moreover, the cheating behaviours, which are conducted by game AIs, destroy the game experience for players. This project aims to develop a terrain analysis architecture, which helps RTS game AI to understand game environment by automated scouting. This may involve exploration strategy, strategic and tactical planning, map-patch management and terrain-feature recognition components.
Chek Tien Tan
C. Si, Y. Pisan, & C. T. Tan, “A Scouting Strategy for Real-Time Strategy Games,” In Proceedings of the 10th Australian Conference on Interactive Entertainment. ACM Press, 2014.
C. Si, Y. Pisan and C. T. Tan, “Automated Terrain Analysis in Real-Time Strategy Games,” in Proc. FDG 2014.
Oct. 27, 2015, 9:31 a.m.