Josh McCoy, Michael Mateas
We present a real-time strategy (RTS) game AI agent that integrates multiple specialist components to play a complete game. Based on an analysis of how skilled human players conceptualize RTS gameplay, we partition the problem space into domains of competence seen in expert human play. This partitioning helps us to manage and take advantage of the large amount of sophisticated domain knowledge developed by human players. We present results showing that incorporating expert high-level strategic knowledge allows our agent to consistently defeat established scripted AI players. In addition, this work lays the foundation to incorporate tactics and unit micro-management techniques developed by both man and machine.
Subjects: 1.8 Game Playing; 2. Architectures
Submitted: Apr 15, 2008