Tactical task allocation and resource management in non-stationary swarm dynamics

The allocation of resources between tasks within a swarm of agents can be difficult without a centralized controller. Disjunctive control has been shown to be a viable method to control the behavior of a swarm. In this project, a disjunctive fuzzy control system is used to solve the problem of resource management. A multi-state swarm is evolved with an offline learning algorithm to adapt to a dynamic scenario with multiple objectives. Some of the emergent behaviors developed through the evolutionary algorithm were state-switching and recruitment techniques.