A variety of game methods in Game Theory: a short survey

Document Type : Review Article

Author

Faculty of Computer Engineering and Information Technology, Shahid Sattari University of Aeronautical Sciences and Technology, Tehran, Iran.

Abstract

In the last decade, the application of game theory in various sciences has grown significantly. Given the increasing growth of IT and artificial intelligence on the battlefield, the strategic interests of each side depend greatly on the smart performance of the other side. Due to the changing nature of battles, which are mainly based on information technology and artificial intelligence, the replacement of new methods in this field can have effective results. In fact, wherever there are limited resources, different decision options, different achievements due to different choices, and the possibility of collaboration or competition between actors, game theory can be used to better analyze the current situation. The purpose of this study is to introduce and define game theory, presentation methods and its types in order to make the best use of the role of game theory on the battlefield. The research method is descriptive based on the nature and method of data collection, because in-depth research is to be done on a specific case (types of games in game theory). The most important results of this research are the knowledge and application of various methods based on game theory, which can lead to increased defensive deterrence on battlefields.

Keywords


[1]. Juve K. The use of massive multiplayer online games to evaluate C4I systems. NAVAL POSTGRADUATE SCHOOL MONTEREY CA; 2004.
[2]. Halpern JY. Computer science and game theory: A brief survey. arXiv preprint cs/0703148. 2007.
[3]. Roy S, Ellis C, Shiva S, Dasgupta D, Shandilya V, Wu Q, editors. A survey of game theory as applied to network security. 2010 43rd Hawaii International Conference on System Sciences; 2010: IEEE.
[4]. Shi H-Y, Wang W-L, Kwok N-M, Chen S-Y. Game theory for wireless sensor networks: a survey. Sensors. 2012;12(7):9055-97.
[5]. Wang Y, Wang Y, Liu J, Huang Z, Xie P, editors. A survey of game theoretic methods for cyber security. 2016 IEEE First International Conference on Data Science in Cyberspace (DSC); 2016: IEEE.
[6]. Do CT, Tran NH, Hong C, Kamhoua CA, Kwiat KA, Blasch E, et al. Game theory for cyber security and privacy. ACM Computing Surveys (CSUR). 2017;50(2):1-37.
[7]. Yang G. Game theory-inspired evolutionary algorithm for global optimization. Algorithms. 2017;10(4):111.
[8]. Habib MA, Moh S. Game theory-based routing for wireless sensor networks: A comparative survey. Applied Sciences. 2019;9(14):2896.
[9]. Liu Z, Luong NC, Wang W, Niyato D, Wang P, Liang Y-C, et al. A survey on applications of game theory in blockchain. arXiv preprint arXiv:190210865. 2019.
[10]. Riahi S, Riahi A. Game theory for resource sharing in large distributed systems. International Journal of Electrical & Computer Engineering (2088-8708). 2019;9(2).
[11].Zargaryan M, Gevorgyan D. Distributed Algorithms and Game Theory.
[12]. Gintis H. Game theory evolving: Princeton university press; 2009.
[13]. Keen D, Andersson R. Double games: Success, failure and the relocation of risk in fighting terror, drugs and migration. Political Geography. 2018;67:100-10.
[14]. Vamvoudakis KG, Lewis FL. Online solution of nonlinear two‐player zero‐sum games using synchronous policy iteration. International Journal of Robust and Nonlinear Control. 2012;22(13):1460-83.
[15]. Bailey JP, Piliouras G. Multi-agent learning in network zero-sum games is a Hamiltonian system. arXiv preprint arXiv:190301720. 2019.
[16]. Jaśkiewicz A, Nowak AS. Non-zero-sum stochastic games. Handbook of dynamic game theory. 2016:1-64.
[17]. Ummels M. Stochastic multiplayer games: Theory and algorithms: Amsterdam University Press; 2010.
[18]. Gámez M, López I, Rodríguez C, Varga Z, Garay J. Game-theoretical model for marketing cooperative in fisheries. Applied Mathematics and Computation. 2018;329:325-38.
[19]. Zhang H, Lian J, Wang H, editors. Improve the Cooperative Level of Population via Individual Recognition Model. 2019 Chinese Control And Decision Conference (CCDC); 2019: IEEE.
[20]. Watson M, Bozgeyikli L, editors. Introduction to Game Theory via an Interactive Gameplay Experience. Companion Publication of the 2019 on Designing Interactive Systems Conference 2019 Companion; 2019.
[21]. Gokhale CS, Traulsen A. Evolutionary multiplayer games. Dynamic Games and Applications. 2014;4(4):468-88.
[22]. Newton J. Evolutionary game theory: A renaissance. Games. 2018;9(2):31.
[23]. Muell MR, Guillory WX, Kellerman A, Rubio AO, Scott‐Elliston A, Morales O, et al. Gaming natural selection: Using board games as simulations to teach evolution. Evolution. 2020;74(3):681-5.
[24]. Cui J, Liu Y, Nallanathan A. Multi-agent reinforcement learning-based resource allocation for UAV networks. IEEE Transactions on Wireless Communications. 2019;19(2):729-43.
[25]. Messous M-A, Senouci S-M, Sedjelmaci H, Cherkaoui S. A game theory based efficient computation offloading in an UAV network. IEEE Transactions on Vehicular Technology. 2019;68(5):4964-74.
[26]. Yan S, Peng M, Cao X. A game theory approach for joint access selection and resource allocation in UAV assisted IoT communication networks. IEEE Internet of Things Journal. 2018;6(2):1663-74.
[27]. Shiri H, Park J, Bennis M, editors. Massive autonomous UAV path planning: A neural network based mean-field game theoretic approach. 2019 IEEE Global Communications Conference (GLOBECOM); 2019: IEEE.
[28]. Zhang K, Yang Z, Başar T. Multi-agent reinforcement learning: A selective overview of theories and algorithms. Handbook of Reinforcement Learning and Control. 2021:321-84.
[29]. Klaine PV, Nadas JP, Souza RD, Imran MA. Distributed drone base station positioning for emergency cellular networks using reinforcement learning. Cognitive computation. 2018;10(5):790-804.
[30]. Gholamnezhad P, Mazloum J. UAV optimal routing based on reference vector guided evolutionary algorithm. Journal of Aeronautical Engineering. 2021;23(1):44-55.
[31]. Kim H, Park J, Bennis M, Kim S-L, editors. Massive UAV-to-ground communication and its stable movement control: A mean-field approach. 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC); 2018: IEEE.
[32]. Xu Y, Ren G, Chen J, Luo Y, Jia L, Liu X, et al. A one-leader multi-follower Bayesian-Stackelberg game for anti-jamming transmission in UAV communication networks. Ieee Access. 2018;6:21697-709.
[33]. Xiao L, Xie C, Min M, Zhuang W. User-centric view of unmanned aerial vehicle transmission against smart attacks. IEEE Transactions on Vehicular Technology. 2017;67(4):3420-30.
[34]. Choudhary G, Sharma V, You I, Yim K, Chen R, Cho J-H, editors. Intrusion detection systems for networked unmanned aerial vehicles: a survey. 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC); 2018: IEEE.
[35]. Saad W, Han Z, Basar T, Debbah M, Hjorungnes A, editors. A selfish approach to coalition formation among unmanned air vehicles in wireless networks. 2009 International Conference on Game Theory for Networks; 2009: IEEE.
[36]. Charlesworth PB, editor Using non-cooperative games to coordinate communications UAVs. 2014 IEEE Globecom Workshops (GC Wkshps); 2014: IEEE.