Applying Goal-Driven Autonomy to StarCraft


  • Ben Weber University of California, Santa Cruz
  • Michael Mateas University of California, Santa Cruz
  • Arnav Jhala University of California, Santa Cruz



Reactive Planning, Goal-Driven Autonomy, Multi-Scale, Real-Time Strategy


One of the main challenges in game AI is building agents that can intelligently react to unforeseen game situations. In real-time strategy games, players create new strategies and tactics that were not anticipated during development. In order to build agents capable of adapting to these types of events, we advocate the development of agents that reason about their goals in response to unanticipated game events. This results in a decoupling between the goal selection and goal execution logic in an agent. We present a reactive planning implementation of the Goal-Driven Autonomy conceptual model and demonstrate its application in StarCraft. Our system achieves a win rate of 73% against the built-in AI and outranks 48% of human players on a competitive ladder server.




How to Cite

Weber, B., Mateas, M., & Jhala, A. (2010). Applying Goal-Driven Autonomy to StarCraft. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 6(1), 101-106.