Dynamics of Task Allocation Based on Game Theory in Multi-Agent Systems

Abstract

Division of labor is a widely studied subject of collective behavior in natural systems. It is concerned with the question of how the regulation of the division of labor may contribute to the performance of the multi-agent system. This brief investigates the evolution of the division of labor with three strategies by employing the evolutionary game theory. Thus, these available strategies are provided for the unstructured multi-agent system: strategy A (performing task A), strategy B (performing task B), and strategy C (not performing any task but only interfere other's actions). Depending on the parameter settings in the payoff matrix, four specific scenarios occur and the corresponding stability of the equilibrium points there are provided by calculation. Numerical calculation results are also provided for an intuitive description of the strategy evolution state. Results show that larger synergistic benefit contributes to the effective division of labor in the system, providing high benefits for the multi-agent system. These results help design a effective mechanism where self-organized division of labor occurs in the form of maximizing the benefit of the multi-agent system.