Learning to Identify Rush Strategies in StarCraft

Abstract : This paper examines strategies used in StarCraft II, a real-time strategy (RTS) game in which two opponents compete in a battlefield context. The RTS genre requires players to make effective strategic decisions. How players execute the selected strategies affects the game result. We propose a method to automatically classify strategies as rush or non-rush strategies using support vector machines (SVMs). We collected game replay data from an online StarCraft II community and focused on high-level players to design the proposed classifier by evaluating four feature functions: (i) the upper bound of variance in time series for the numbers of workers, (ii) the upper bound of the numbers of workers at a specific time, (iii) the lower bound of the start time to build a second base, and (iv) the upper bound of the start time to build a specific building. By evaluating these features, we obtained the parameters combinations required to design and construct the proposed SVM-based rush identifier. Then we implemented our findings into a StarCraft: Brood War (StarCraft I) agent to demonstrate the effectiveness of the proposed method in a real-time game environment.
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Teguh Budianto, Hyunwoo Oh, Takehito Utsuro. Learning to Identify Rush Strategies in StarCraft. 17th International Conference on Entertainment Computing (ICEC), Sep 2018, Poznan, Poland. pp.90-102, ⟨10.1007/978-3-319-99426-0_8⟩. ⟨hal-02128626⟩

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