Sequential Pattern mining in StarCraft:Brood War for short and long-term goals

A wide variety of strategies have been used to create agents in the growing field of real-time strategy AI. However, a frequent problem is the necessity of handcrafting competencies, which becomes prohibitively difficult in a large space with many comer cases. A preferable approach would be to learn these competencies from the wealth of expert play available. We present a system that uses the Generalized Sequential Pattern (GSP) algorithm from data mining to find common patterns in StarCraft:Brood War replays at both the micro-and macro-level, and verify that these correspond to human understandings of expert play. In the future, we hope to use these patterns to learn tasks and goals in an unsupervised manner for an HTN planner.

Duke Scholars

Author Arnav Jhala Engineering Graduate and Professional Programs