Abstract
One of the key advantages of unmanned swarm operation is its autonomous cooperation. When the communication is interrupted or the centralized control manner is lost, the cooperative operation can still be carried out orderly. This work proposed a cooperative evolution mechanism within the framework of multiplayer public goods game to solve the problem of autonomous collaboration of unmanned swarm in case of failure of centralized control. It starts with the requirement analysis of autonomous cooperation in unmanned swarm, and then, the evolutionary game model of multiplayer public goods based on aspiration-driven dynamics is established. On this basis, the average abundance function is constructed by theoretical derivation, and furthermore, the influence of cost, multiplication factor, and aspiration level on the average abundance is simulated. Finally, the evolutionary mechanism of parameter adjustment in swarm cooperation is revealed via case study, and deliberate proposals are suggested to provide a meaningful exploration in the actual control of unmanned swarm cooperation.
Highlights
With the continuous advancing of the third wave of artificial intelligence, “group evolutionary intelligence” developed from “single-agent autonomous intelligence” has become one of the important characteristics of the new generation of artificial intelligence
In the military field, unmanned swarm operations have received unprecedented attention over the past two years. e US military has listed unmanned swarm operations as a “subversive technology” that can change the rules of war
The centralized control mode fails, and the unmanned swarm must make effective response on the spot according to the external situation and achieve self-management and self-coordination
Summary
With the continuous advancing of the third wave of artificial intelligence, “group evolutionary intelligence” developed from “single-agent autonomous intelligence” has become one of the important characteristics of the new generation of artificial intelligence. Ere are mainly two kinds of control modes of unmanned swarm: centralized control and autonomous collaboration. In the complex electromagnetic environment of the battlefield, there is a real risk of communication failure [4] In such a predicament, the centralized control mode fails, and the unmanned swarm must make effective response on the spot according to the external situation and achieve self-management and self-coordination. An issue that has led to considerable interest is how unmanned swarms autonomously and cooperatively complete established military operations. Overall planning and reallocation of operation resources (communication, firepower, intelligence, etc.) within the unmanned swarms is required when autonomous collaboration occurs. In the fire strike task, the “rational” unmanned units with intelligence and decision-making ability will choose to “contribute” ammunition to the swarm as little as possible in order to maintain its combat effectiveness, while on the Mathematical Problems in Engineering. Other hand, the more ammunition each unit contributes to the swarm, the higher the survival rate and the greater the combat effectiveness of the whole swarm will be. e contradiction between the two will lead to “tragedy of the commons” [5]; how to increase the number of units’ willing to positively contribute ammunition to the swarm and avoid the tragedy has become a crucial and urgent problem in both technology research and practical application of unmanned swarm
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