TACTICAL AI IN REAL TIME STRATEGY GAMES

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. Not only must
a variety of unit maneuvers for each unit taking part in a battle be analyzed, the
decision must be made before the battlefield changes to the point that the decision is
no longer viable. 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 t 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.