Applying LCS/XCS to the RTS Games Domain

Abstract

Real-Time Strategy games (RTS) are representatives of the highest class of computational complexity in computer game genres. To cope with the high complexity of the state-action space of RTS game worlds, various Machine Learning algorithms are being used and researched extensively. In this article, we apply eXtended Classifier Systems (XCS) to the domain of RTS games. The XCS algorithm belongs to a Learning Classifier Systems (LCS) group known for their adaptability, generalisation, and scalability. We build the game agent named AIXCS. It uses a group of XCS algorithms, which generate a set of unit-actions used in the RTS game. The AIXCS operates without prior learning from the game runs and in tight timing constraints. The AIXCS was put to the test against other game agents in the micro RTS game environment, with positive results regarding successful game operation at runtime.

Keywords

• AI
• game agent
• LCS
• micro RTS
• real-time strategy games
• XCS