Competitive Evolution of Tactical Multiswarm Dynamics

Abstract—The dynamics of large decentralized groups of agents, or swarms, can be difficult to characterize due to complex and often unpredictable behaviors that arise from low-level inter-actions between agents. When designing multiagent systems, these emergent behaviors can have hidden and undesirable implications on the overall operation of the swarm. This paper examines the use of inversion of swarm dynamics to refine individual agents’ rules of operation in order to achieve a given collective goal and applies this method to a scenario of tactical relevance: the point defense of a very important person between two attacking and defending swarms. An alternating competitive evolution is used in a toggled behavioral arms race in order to refine tactics and anticipate counteractions. Results include creative solutions with varying levels of success at addressing defensive tactical scenarios, with the attacking swarms evolving behaviors (such as rushing, splitting, and baiting) and the defending swarm evolving proactive and reactive solutions. Index Terms—Competitive evolution, genetic algorithm, multiagent systems, swarm intelligence. I