  We attack the problem of controlling teams of autonomous units during
skirmishes in real-time strategy games. Earlier work had shown promise in
evolving control algorithm parameters that lead to high performance team
behaviors similar to those favored by good human players in real-time strategy
games like Starcraft. This algorithm specifically encoded parameterized kiting
and fleeing behaviors and the genetic algorithm evolved these parameter values.
In this paper we investigate using influence maps and potential fields alone to
compactly represent and control real-time team behavior for entities that can
maneuver in three dimensions. A two-objective fitness function that maximizes
damage done and minimizes damage taken guides our multi-objective evolutionary
algorithm. Preliminary results indicate that evolving friend and enemy unit
potential field parameters for distance, weapon characteristics, and entity
health suffice to produce complex, high performing, three-dimensional, team
tactics.
