Evolutionary game dynamics of multi-agent cooperation driven by self-learning

Abstract

Multi-agent cooperation problem is a fundamental issue in the coordination control field. Individuals achieve a common task through association with others or division of labor. Evolutionary game dynamics offers a basic framework to investigate how agents self-adaptively switch their strategies in accordance with various targets, and also the evolution of their behaviors. In this paper, we analytically study the strategy evolution in a multiple player game model driven by self-learning. Self-learning dynamics is of importance for agent strategy updating yet seldom analytically addressed before. It is based on self-evaluation, which applies to distributed control. We focus on the abundance of different strategies (behaviors of agents) and their oscillation (frequency of behavior switching). We arrive at the condition under which a strategy is more abundant over the other under weak selection limit. Such condition holds for any finite population size of ≥ 3, thus it fits for the systems with finite agents, which has notable advantage over that of pairwise comparison process. At certain states of evolutionary stable state, there exists "ping-pong effect" with stable frequency, which is not affected by aspirations. Our results indicate that self-learning dynamics of multi-player games has special characters. Compared with pairwise comparison dynamics and Moran process, it shows different effect on strategy evolution, such as promoting cooperation in collective risk games with large threshold.

Cooperative Evolution Mechanism of Unmanned Swarm within the Framework of Public Goods Game

“…Nevertheless, there are still two shortcomings in the above achievements in solving the issue of cooperative evolution of unmanned swarms: first, it has not focused on the public goods game; that is, although there are similarities between the snowdrift game and public goods game [31], there are essential differences in the game mechanism; in addition, the cooperative evolution of unmanned swarm is the multiple interaction of combat units; that is, the evolution result is not only related to the strategy selection of single unit, but also depends on the strategy of other units in the swarm, which is characterized by multiplayer games [32,33]. So far, the academic community has mastered the payoff matrix [34] of the public goods game with multiplayer and has simulated the influence of different selection intensity [35][36][37] and threshold values [31] on the cooperation level. In particular, in literature [34], the authors derived a general average abundance formula of multiparty games in a finite population under aspiration-driven dynamics, which can be applicable to any multiparty game under the aspiration-driven dynamics of a finite population.…”

The characteristics of average abundance function with mutation of multi-player threshold public goods evolutionary game model under redistribution mechanism

“…In addition, aspiration level promotes the heterogeneity distribution of node degree. Du et al [29] deduced the dominant condition for average abundance function under aspiration rule. Perc et al [30] affirmed that heterogeneity in aspirations may be key for the sustainability of cooperation in structured populations.…”

Strategy dominance mechanism of autonomous collaboration in unmanned swarm within the framework of public goods game

“…Du et al applied the results of Tarnita et al [23] in conjunction with a statistical analysis and computer simulation to find that the average abundance was independent of the aspiration level under a weak selection intensity [24]. Furthermore, Du et al extended this theoretical result to determine the strategy dominance condition for the multiplayer game under a weak selection intensity [25,26].…”