SORTS: A Human-Level Approach to Real-Time Strategy AI


  • Sam Wintermute University of Michigan
  • Joseph Xu University of Michigan
  • John E. Laird University of Michigan



We developed knowledge-rich agents to play real-time strategy games by interfacing the ORTS game engine to the Soar cognitive architecture. The middleware we developed supports grouping, attention, coordinated path finding, and FSM control of low-level unit behaviors. The middleware attempts to provide information humans use to reason about RTS games, and facilitates creating agent behaviors in Soar. Agents implemented with this system won two out of three categories in the AIIDE 2006 ORTS competition.




How to Cite

Wintermute, S., Xu, J., & Laird, J. (2021). SORTS: A Human-Level Approach to Real-Time Strategy AI. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 3(1), 55-60.