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

doi:10.23919/JSEE.2023.000131

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (5): 1252-1266.doi: 10.23919/JSEE.2023.000131

• Systems Engineering • Previous Articles Next Articles

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

Li PAN¹^,²(), Zhonghong WU¹(), Minggang YU³^,*(), Jintao LIU³(), Dan MEI⁴()

¹ Weaponry Engineering College, Naval University of Engineering, Wuhan 430000, China
² Electronics Engineering College, Naval University of Engineering, Wuhan 430000, China
³ Coastal Defense College, Naval Aeronautical University, Yantai 264001, China
⁴ Qingdao Campus, Naval Aeronautical University, Qingdao 266000, China

Received:2021-08-08 Online:2023-10-18 Published:2023-10-30
Contact: Minggang YU E-mail:329674406@qq.com;yizhousan@163.com;yuminggang8989@163.com;jintao_liu_2020@126.com;meidan_plan@126.com
About author:
PAN Li was born in 1985. She is a lecturer of Electronics Engineering College, Naval University of Engineering of the PLA. She has participated in the research work of several R&D Program of PLA Navy. She is the author of four books. Her research interests are SoS engineering and wireless communication theory. E-mail: 329674406@qq.com

WU Zhonghong was born in 1988. He is a lecturer of Weaponry Engineering College, Naval University of Engineering of the PLA. He has published more than 20 papers in important journals and academic conferences in the research field, including three SCI indexed, five EI indexed, and he also is the author of seven books. He has chaired one Natural Science Foundation of Hubei Province, participated in the research work of several National Natural Science Foundation of China, R&D Program of the PLA, and received two awards of the Scientific and Technological Progress. His research interests are robot pose estimation and theory of intelligent command and control. E-mail: yizhousan@163.com

YU Minggang was born in 1986. He is an associate professor of Institute of Command and Control Engineering, Army Engineering University of the PLA. He has published more than 50 papers in important journals and academic conferences in the research field, including 10 SCI indexed and 15 EI indexed, and he is also the author of five books. He has chaired one National Natural Science Foundation of China, participated in the research work of several National Science and Technology Ministry Key Research and Development Plan, National Key R&D Program of China, China Academy of Engineering Consulting Research Project, and received three Awards of the Scientific and Technological Progress. His research interests are SoS engineering and theory of intelligent command and control. E-mail: yuminggang8989@163.com

LIU Jintao was born in1981. He received his Ph.D. degree in control science and engineering from Naval Aeronautical University and later conducted postdoctoral research in computer science and technology at the Army Engineering University. He is with the Coastal Defense College, Naval Aeronautical University. His research interests include unmanned aerial vehicle flight control and unmanned aerial vehicle swarm control. E-mail: jintao_liu_2020@126.com

MEI Dan was born in 1981. He is currently with Qingdao Campus, Naval Aeronautical University, Yantai, China. He has published more than 10 papers and two works in important journals and conferences in this field. He has presided over and participated in a number of key naval research and development programs, and was awarded two scientific and technological progress awards. His research interests are unmanned aerial vehicle (UAV) equipment support and technology applications. E-mail: meidan_plan@126.com
Supported by:
This work was supported by the National Natural Science Foundation of China (71901217) and the National Key R&D Program of China (2018YFC0806900).

Abstract

Abstract:

The key advantage of unmanned swarm operation is its autonomous cooperation. How to improve the proportion of cooperators is one of the key issues of autonomous collaboration in unmanned swarm operations. This work proposes a strategy dominance mechanism of autonomous collaboration in unmanned swarm within the framework of public goods game. It starts with the requirement analysis of autonomous collaboration in unmanned swarm; and an aspiration-driven multiplayer evolutionary game model is established based on the requirement. Then the average abundance function and strategy dominance condition of the model are constructed by theoretical derivation. Furthermore, the evolutionary mechanism of parameter adjustment in swarm cooperation is revealed via simulation, and the influences of the multiplication factor $ r $ , aspiration level $ \alpha $ , threshold $ m $ and other parameters on the strategy dominance conditions were simulated for both linear and threshold public goods games (PGGs) to determine the strategy dominance characteristics; Finally, deliberate proposals are suggested to provide a meaningful exploration in the actual control of unmanned swarm cooperation.

Key words: unmanned combat swarm, autonomous collaboration, strategy dominance, multi-player public goods game (PGG)

Li PAN, Zhonghong WU, Minggang YU, Jintao LIU, Dan MEI. Strategy dominance mechanism of autonomous collaboration in unmanned swarm within the framework of public goods game[J]. Journal of Systems Engineering and Electronics, 2023, 34(5): 1252-1266.

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Autonomous collaboration of unmanned swarm	Multi-player public goods evolutionary game
Unmanned swarm	Mixed homogeneous population
Public resources required by autonomous collaboration (ammunition, communication, etc.)	Public goods
Multiple unmanned platforms involving in autonomous collaboration	Multi-player
Individual unmanned platforms	Individuals
Individual unmanned platforms as research objects	Focal individual
Cooperative behavior where platforms are willing to contribute resources to the swarm	Strategy A
Non-cooperative behavior where platforms prefer “free-riding” rather than contributing resources to the swarm	Strategy B
Public resources the swarm gives back to platforms under different strategies	Pay-off
Payoff-based strategy transition between unmanned platforms	Game
Dynamic variations in the proportion of platforms that take different strategies in multiple rounds of game	Evolutionary
After multiple rounds, the game is terminated after the proportion of platforms stabilizes	Evolutionary stable state
Expected proportion of cooperators (non-cooperators) in the swarm in the evolutionary stable state	Average abundance
Average abundance of cooperators in the swarm is larger than 0.5	Strategy A dominates

Strategy	Number of cooperators
Strategy	d−1	···	k−1	···	1	0
A	rc−c	···	[r(kc+c)/d]−c	···	[2rc/d]−c	[rc/d]−c
B	r(d−1)c/2	···	rkc/d	···	rc/d	0

Strategy	Number of cooperators
Strategy	d−1	···	k	···	m−1	···	0
A	rc	···	[r(kc+c)/d]−c	···	mrc/d	···	0
B	[r(d−1)c/d]+c	···	[rkc/d]+c	···	0	···	0

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

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