Tactical AI in Real Time Strategy Games

Abstract

The real time strategy (RTS) tactical decision making problem is a difficult problem. It is generally more complex due to its high degree of time sensitivity. This research effort presents a novel approach to this problem within an educational, teaching objective. Particular decision focus is target selection for a artificial intelligence (AI) RTS game model. The use of multi-objective evolutionary algorithms (MOEAs) in this tactical decision making problem allows an AI agent to make fast, effective solutions that do not require modification to the current environment. This approach allows for the creation of a generic solution building tool that is capable of performing well against scripted opponents without requiring expert training or deep tree searches. The experimental results validate that MOEAs can control an on-line agent capable of out performing a variety AI RTS opponent test scripts