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.
Good micro for skirmishes in battle or more generally, coordinated group behavior for adaptive agents, has applications in many fields from wargaming and modeling industrial agents, to robotics, simulations, and video games @cite_14 @cite_36 @cite_33 @cite_35 @cite_31 @cite_7 @cite_28 @cite_16 @cite_17 @cite_4 . From flocking, swarming, and other distributed control algorithms @cite_18 @cite_17 drawn from observing natural behavior to evolutionary algorithms that tune the parameters of potential fields that control the movement of autonomous vehicles @cite_14 , much research has been done in the area of group tactics, adaptive behavior, and distributed AI. In RTS games, there is strong research interest in generating good micro since this can lead to your winning skirmishes even when your forces are outnumbered or otherwise disadvantaged. This paper focuses on an evolutionary computing approach to generating coordinated autonomous behaviors (micro) for groups of computer game units (or entities). Specifically, we use an influence map and several potential fields to represent entity disposition in order to evolve effective group tactics, or good micro, for controlling a group of entities battling an opposing group of entities. Good micro maximizes damage to enemy units while minimizing damage to friendly units.
@cite_35 @cite_18 @cite_14 @cite_4 @cite_33 @cite_7 @cite_36 @cite_28 @cite_31 @cite_16 @cite_17
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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. 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 real-time strategy games (RTS), the player must reason about high-level strategy and planning while having effective tactics and even individual units micro-management. Enabling an artificial agent to deal with such a task entails breaking down the complexity of this environment. For that, we propose to control units locally in the Bayesian sensory motor robot fashion, with higher level orders integrated as perceptions. As complete inference encompassing global strategy down to individual unit needs is intractable, we embrace incompleteness through a hierarchical model able to deal with uncertainty. We developed and applied our approach on a StarCraft1 AI. Creating strong AI forces in military war simulations or RTS video games poses many challenges including partially observable states, a possibly large number of agents and actions, and simultaneous concurrent move execution In this paper we consider a tactical sub–problem that needs to be addressed on the way to strong computer generated forces: abstract combat games in which a small number of inhomogeneous units battle with each other in simultaneous move rounds until all members of one group are eliminated We present and test several adversarial heuristic search algorithms that are able to compute reasonable actions in those scenarios using short time controls Tournament results indicate that a new algorithm for simultaneous move games which we call “randomized alpha–beta search” (RAB) can be used effectively in the abstract combat application we consider In this application it outperforms the other algorithms we implemented We also show that RAB's performance is correlated with the degree of simultaneous move interdependence present in the game.