  We investigate an evolutionary multi-objective approach to good micro for
real-time strategy games. Good micro helps a player win skirmishes and is one
of the keys to developing better real-time strategy game play. In prior work,
the same multi-objective approach of maximizing damage done while minimizing
damage received was used to evolve micro for a group of ranged units versus a
group of melee units. We extend this work to consider groups composed from two
types of units. Specifically, this paper uses evolutionary multi-objective
optimization to generate micro for one group composed from both ranged and
melee units versus another group of ranged and melee units. Our micro behavior
representation uses influence maps to represent enemy spatial information and
potential fields generated from distance, health, and weapons cool down to
guide unit movement. Experimental results indicate that our multi-objective
approach leads to a Pareto front of diverse high-quality micro encapsulating
multiple possible tactics. This range of micro provided by the Pareto front
enables a human or AI player to trade-off among short term tactics that better
suit the player's longer term strategy - for example, choosing to minimize
friendly unit damage at the cost of only lightly damaging the enemy versus
maximizing damage to the enemy units at the cost of increased damage to
friendly units. We believe that our results indicate the usefulness of
potential fields as a representation, and of evolutionary multi-objective
optimization as an approach, for generating good micro.
