Potential game for dynamic task allocation in multi-agent system

This paper proposes a novel distributed multi-agent dynamic task allocation method based on the potential game. Consider that the workload of each task may vary in a dynamic environment, and the communication range of each agent constrains the selectable action set. Each agent makes the decision independently based on the local information. Firstly, a potential game-theoretic framework is designed. Any Nash equilibrium is guaranteed at least 50% of suboptimality, and the best Nash equilibrium is the optimal solution. Furthermore, a time variant constrained binary log-linear learning algorithm is provided and the global convergence is proved under certain conditions. Finally, numerical results show that the proposed algorithm performs well in terms of global searching ability, and verify the effectiveness of the distributed dynamic task allocation approach.