Ponder-reinforcement cooperative algorithm for multi-agent foraging task based on evolutionary stable equilibrium

Algorithms based on game theory regard the equilibriums as the optimal solution for the cooperation in multi-agent system (MAS), especially the evolutionary stable equilibriums (ESE) had been studied because they can give a consistent optimal solution for the MAS and partly solve the equilibrium selection problem of game theory. However ESE is dynamic stable, so the strategy of every agent keeps on changing. Consequently, the degree of the convergence is lower and the process of convergence is long. A reinforcement factor was introduced to strength the influence of the utilities of strategies, and then a ponder-reinforcement algorithm was proposed to accelerate the process of convergence and to improve the stability of convergent ESE. The multi-agent foraging simulations verified the efficiency of the proposed algorithm.