Mining Balanced Sequential Patterns In Rts Games

The video game industry has grown enormously over the last twenty years, bringing new challenges to the artificial intelligence and data analysis communities. We tackle here the problem of automatic discovery of strategies in real-time strategy games through pattern mining. Such patterns are the basic units for many tasks such as automated agent design, but also to build tools for the professionally played video games in the electronic sports scene. Our formalization relies on a sequential pattern mining approach and a novel measure, the balance measure, telling how a strategy is likely to win. We experiment our methodology on a real-time strategy game that is professionally played in the electronic sport community.