Applying Goal-Driven Autonomy to StarCraft

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.

• Publication year

  2010

• Venue

  Artificial Intelligence and Interactive Digital Entertainment Conference

• Publication date

  2010-10-10

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1609/aiide.v6i1.12401
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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