Introducing Hierarchical Adversarial Search, a Scalable Search Procedure for Real-Time Strategy Games

Real-Time Strategy (RTS) video games have proven to be a very challenging application area for Artificial Intelligence research. Existing AI solutions are limited by vast state and action spaces and real-time constraints. Most implementations efficiently tackle various tactical or strategic sub-problems, but there is no single algorithm fast enough to be successfully applied to full RTS games. This paper introduces a hierarchical adversarial search framework which implements a different abstraction at each level — from deciding how to win the game at the top of the hierarchy to individual unit orders at the bottom.