Current Issue : October - December Volume : 2020 Issue Number : 4 Articles : 5 Articles
Based on the traditional propagation model, this paper innovatively divides nodes into high- and low-energy states through\nintroducing Low-energy (L) state and presents a whole new propagation model which is more suitable for WSNs (wireless sensor\nnetworks) against malicious programs, namely, SILRD (Susceptible, Infected, Low-energy, Recovered, Dead) model. In this paper,\nnodes are divided into five states according to the residual energy and infection level, and the differential equations are constructed\nto describe the evolution of nodes. At the same time, aiming at the exhaustion of WSNsâ?? energy, this paper introduces charging as a\nmethod to supplement the energy. Furthermore, we regard the confrontation between WSNs and malicious programs as a kind of\ngame and find the optimal strategies by using the Pontryagin Maximum Principle. It is found that charging as a defense\nmechanism can inhibit the spread of malicious programs and reduce overall costs. Meanwhile, the superiority of bang-bang\ncontrol on the SILRD model is highlighted by comparing with square control....
In a one-on-one air combat game, the opponentâ??s maneuver strategy is usually not deterministic, which leads us to consider a\nvariety of opponentâ??s strategies when designing our maneuver strategy. In this paper, an alternate freeze game framework based on\ndeep reinforcement learning is proposed to generate the maneuver strategy in an air combat pursuit. Themaneuver strategy agents\nfor aircraft guidance of both sides are designed in a flight level with fixed velocity and the one-on-one air combat scenario.\nMiddleware which connects the agents and air combat simulation software is developed to provide a reinforcement learning\nenvironment for agent training. A reward shaping approach is used, by which the training speed is increased, and the performance\nof the generated trajectory is improved. Agents are trained by alternate freeze games with a deep reinforcement algorithm to deal\nwith nonstationarity. A league system is adopted to avoid the red queen effect in the game where both sides implement adaptive\nstrategies. Simulation results show that the proposed approach can be applied to maneuver guidance in air combat, and typical\nangle fight tactics can be learnt by the deep reinforcement learning agents. For the training of an opponent with the adaptive\nstrategy, the winning rate can reach more than 50%, and the losing rate can be reduced to less than 15%. In a competition with all\nopponents, the winning rate of the strategic agent selected by the league system is more than 44%, and the probability of not losing\nis about 75%....
This paper studies a class of cooperative games, called graphical cooperative games, where the internal topology of the coalition\ndepends on a prescribed communication graph among players. First, using the semitensor product of matrices, the value function\nof graphical cooperative games can be expressed as a pseudo-Boolean function. Then, a simple matrix formula is provided to\ncalculate the Shapley value of graphical cooperative games. Finally, some practical examples are presented to illustrate the\napplication of graphical cooperative games in communication-based coalitions and establish the significance of the Shapley value\nin different communication networks....
This paper considers a Cournot-Bertrand game model based on the relative profit maximization with bounded rational players.\nThe existence and stability of the Nash equilibrium of the dynamic model are investigated. The influence of product differentiation\ndegree and the adjustment speed on the stability of the dynamic system is discussed. Furthermore, some complex properties and\nglobal stability of the dynamic system are explored. The results find that the higher degree of product differentiation enlarges the\nstable range of the dynamic system, while the higher unit product cost decreases the stable range of price adjustment and increases\nthe one of output adjustment; period cycles and aperiodic oscillation (quasi-period and chaos) occur via period-doubling or\nNeimarkâ??Sacker bifurcation, and the attraction domain shrinks with the increase of adjustment speed values. By selecting\nappropriate control parameters, the chaotic system can return to the stable state. The research of this paper is of great significance\nto the decision-makersâ?? price decision and quantity decision....
In recent years, evolutionary game theory has been gradually applied to analyze and predict network attack and defense for\nmaintaining cybersecurity. The traditional deterministic game model cannot accurately describe the process of actual network\nattack and defense due to changing in the set of attack-defense strategies and external factors (such as the operating environment\nof the system). In this paper, we construct a stochastic evolutionary game model by the stochastic differential equation with\nMarkov property. The evolutionary equilibrium solution of the model is found and the stability of the model is proved according\nto the knowledge of the stochastic differential equation. And we apply the explicit Euler numerical method to analyze the\nevolution of the strategy selection of the players for different problem situations. The simulation results show that the stochastic\nevolutionary game model proposed in this paper can get a steady state and obtain the optimal defense strategy under the action of\nthe stochastic disturbance factor. In addition, compared with other kinds of literature, we can conclude that the return on security\ninvestment of this model is better, and the strategy selection of the attackers and defenders in our model is more suitable for actual\nnetwork attack and defense....
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