Task Allocation for Heterogeneous Multi-agent System via Game-Based Learning

Abstract

In this paper, heterogeneous multi-agent system is employed to perform tasks. We propose a novel task allocation method based on potential game under the limited communication range of each agent. Firstly, the task allocation problem is formulated as a potential game with the goal of maximizing task utility. Then we confirm the existence of Nash Equilibrium (NE). Furthermore, a concurrent best response algorithm with rapid convergence is proposed to achieve the desirable solution. Finally, the simulation results show the effectiveness of the proposed algorithm.