Learning Probabilistic Behavior Models in Real-Time Strategy Games


We study the problem of learning probabilistic models of high-level strategic behavior in the real-time strategy (RTS) game StarCraft. The models are automatically learned from sets of game logs and aim to capture the common strategic states and decision points that arise in those games. Unlike most work on behavior/strategy learning and prediction in RTS… (More)


5 Figures and Tables


Citations per Year

66 Citations

Semantic Scholar estimates that this publication has 66 citations based on the available data.

See our FAQ for additional information.

Slides referencing similar topics