Task allocation in multi-agent systems using models of motivation and leadership

The paper considers the task allocation problem in the case where there is a small number of agents initialized at a single point. The objective is to achieve an even distribution of agents to tasks. To address this problem, this paper proposes a new method that endows agents with models of motivation and leadership to aid their coordination. The proposed approach uses the Particle Swarm Optimization algorithm with a ring neighborhood topology as a foundation and incorporates computational models of motivation to achieve the goals of task allocation more effectively. Simulation results show that, first, the proposed method increases the number of tasks discovered. Secondly, the number of tasks to which the agents are allocated increases. Thirdly, the agents distribute themselves more evenly among the tasks.