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.
Within the games community, Yannakakis @cite_21 evolved opponent behaviors while Doherty @cite_5 evolved tactical team behavior for teams of agents. Avery used an evolutionary computing algorithm to generate influence map parameters that led to effective group tactics for teams of entities against a fixed opponent @cite_17 @cite_28 . We define potential fields and influence maps in more detail later in the paper. This paper extends Liu @cite_16 and Louis' @cite_7 work in dealing with micro for heterogeneous groups of units.
@cite_7 @cite_28 @cite_21 @cite_5 @cite_16 @cite_17
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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. In this paper we introduce experiments on neuro-evolution mechanisms applied to predator prey multi-character computer games. Our test-bed is a modified version of the well-known Pac-Man game. By viewing the game from the predators’ (i.e. opponents’) perspective, we attempt off-line to evolve neural-controlled opponents capable of playing effectively against computer-guided fixed strategy players. However, emergent near-optimal behaviors make the game less interesting to play. We therefore discuss the criteria that make a game interesting and, furthermore, we introduce a generic measure of predator prey computer games’ interest. Given this measure, we present an evolutionary mechanism for opponents that keep learning from a player while playing against it (i.e. on-line) and we demonstrate its efficiency and robustness in increasing and maintaining the game’s interest. Computer game opponents following this on-line learning approach show high adaptability to changing player strategies which provides evidence for the approach’s effectiveness against human players. We are interested in using genetic algorithms to generate winning maneuvering behaviors (or micro) in skirmish scenarios for three dimensional Real-Time Strategy games. In prior work, we encoded parameterized 3D micro behaviors like target selection and kiting into an algorithm for controlling friendly units in battle. Genetic algorithms then tuned these parameters to guide unit maneuvering in order to win skirmishes. In this study, we investigate a new representation for micro behaviors that uses only an influence map and a combination of thirteen potential fields. Genetic algorithms then tune influence map and potential field parameters to evolve winning micro behaviors. We compare the performance of both representations on identical scenarios against identical opponents in a full 3D RTS game environment called FastEcslent. The results show that the genetic algorithm using our new representation using less domain knowledge, reliably evolved high quality 3D micro behaviors that slightly, but significantly, outperformed behaviors from our prior work. Our work thus provides evidence for the viability of using potential fields for generating high quality, complex, micro for three dimensional RTS games. In this paper we successfully demonstrate the use of coevolving Influence Maps (IM)s to generate coordinating team tactics for a Real Time Strategy (RTS) game. Each entity in the team is assigned their own IM generated with evolved parameters. The individual IMs allows each entity to act independently of the team and team coordination is then achieved by evolving all team entities' IM parameters together as a single chromosome with a single evaluation. These evolved parameters are then evaluated by measuring performance against another coevolving population of individuals. Using this method we have generated some interesting strategies, and have demonstrated the potential of using IMs for coevolving team tactics. In the future, coevolved tactics could then be used to provide a challenging opponent for human players.