Abstract
From an artificial intelligence point of view, Real-Time Strategy (RTS) game has been proven to be one of the most challenging areas. Due to the huge action state space, partial observability and real time property, the previous AI solutions are sill low in terms of speed. In this paper, we propose a Cluster-based Alpha-Beta Considering Durations (CABCD) algorithm that searches the optimal action for each unit based on the cluster. When the improved algorithm is applied to large RTS games, the enormous branching factors are significantly reduced. The approach is evaluated in StarCraft I, and outperforms competing methods promisingly.
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