基于演化博弈的蜜罐有效性机理证明

借鉴自然界生物演变进化过程中复制动态的思想,基于演化博弈对蜜罐技术的有效性机理进行研究,分析网络中攻防双方如何根据自身行动策略及支付函数进行演变,从而使博弈收益最大化。演化博弈从一种全新角度诠释了博弈均衡概念,不再是完全理性也非完全信息,为纳什均衡和均衡战略的选择演绎出新方法。演化博弈过程中,防御方是包括普通服务和蜜罐的混合系统,其对手是访问混合系统的恶意攻击者,双方构成了博弈参与者。混合网络系统可看作一个生态系统,而来访者则只有攻击者一个种群;混合系统持续为来访者提供服务,攻击者可选择访问或不访问。论文基于复制动态方程推理计算满足演化稳定策略的均衡点,并利用Matlab平台仿真验证博弈双方的策略演变趋势,从而在理论上证明了蜜罐技术的有效性机理。     

802.11网络中基于博弈理论的可变带宽信道分配研究

802.11网络中节点的理性和自私性导致可变带宽信道分配的低公平性、低负载均衡性及低社会效率问题.基于非合作博弈理论将可变带宽信道分配问题建模成策略型博弈模型.首先,给出问题的纳什均衡分配策略,证明了纳什均衡点的存在;然后,针对纳什均衡策略社会效率低的问题,提出一种基于支付的激励机制,使可变带宽信道分配过程收敛到占优决策均衡状态,从而系统整体吞吐量性能达到全局最优;并分析了上述两种策略的公平性和负载均衡问题;最后,给出达到纳什均衡和全局最优状态的可变带宽信道分配算法.仿真结果表明,纳什均衡策略能够获得好的公平性,而全局最优策略的负载均衡和社会效率性能要优于纳什均衡策略.     

N人合作博弈的Nash及演化均衡稳定策略分析

在有限理性的基础上,对N人合作博弈的对称Nash均衡进行了分析,并引入演化博弈理论分析了参与人的演化均衡稳定策略,得到了不同策略选择下的均衡点。进而应用生物复制动态理论对离散时间及连续时间下的复制动态稳定集进行了研究。最后通过实例说明了该方法在博弈均衡选择上的有效性。     

基于激励机制的网络攻防演化博弈模型研究

随着网络信息系统的日益复杂化,网络的安全性和用户隐私性引起了人们的高度重视,寻找能够维护网络安全、分析和预判网络攻防形式的新技术尤为重要.由于演化博弈理论的特性与网络攻防的特性较为契合,因此,本文对网络环境进行了分析,构建网络攻防场景,并在惩罚机制的基础上引入激励机制,提出了基于激励机制的攻防演化博弈模型.通过给出群体不同的问题情境,利用复制动态方程对局中人的策略选取进行演化分析.另外,在第三方监管部门对局中人管理的基础上,分析不同攻击时长时攻击群体的演化规律,证明攻击具有时效性.通过激励机制对防御群体策略选取的影响以及引入防御投资回报,来进一步证明增加激励机制的可行性.根据实验验证表明,本文提出的攻防演化博弈模型在不同的问题情境下均可达到稳定状态并获得最优防御策略,从而有效减少防御方的损失,遏制攻击方的攻击行为.     

资源受限移动边缘计算任务拆分卸载调度决策

研究带有计算访问点的多用户移动边缘计算环境中的多任务调度与卸载决策问题。为了降低移动设备端的能耗,并确保用户任务的延时需求,提出一种基于博弈论的任务卸载决策算法。为了求解博弈模型,将卸载博弈模型转换为势博弈模型,进而证明博弈存在纳什均衡解,并设计一种基于有限改进性质的分布式博弈方法寻找该纳什均衡解。实验结果证明,在不同的起始策略组合条件下,该博弈算法可以得到相对于对比算法更接近于理论最优解的系统总体最优代价。     

基于演化博弈的多用户网络选择算法

无线通信技术的发展和演进,使得多种广域蜂窝网和大量无线局域网共存、重叠。针对热点区域,密集分布的大量用户同时发起同种业务请求应用场景,提出一种基于演化博弈的多用户网络选择算法,依据选择网络的用户数设计效用函数,给出了演化博弈的复制动态方程。与RSSI算法的对比仿真结果表明：该算法能快速达到演化均衡,用户平均收益高于RSSI算法,接入网络的用户分布更均衡,能合理利用网络资源。     

认知无线电中基于博弈论的频谱分配算法*

利用博弈论分析了认知无线电网络中动态频谱分配问题,构建了基于博弈论的认知无线电频谱分配问题模型,提出了基于潜在博弈论的分布式频谱分配算法,并得到了相应博弈过程的纳什均衡。仿真结果表明,该算法能在较短时间内收敛到稳定状态,潜在函数取值达到最大值、系统总干扰水平降到最小、用户的SIR水平得到明显改善,达到了潜在博弈下信道分配的纳什均衡,实现了提高频谱利用率的目的。     

不确定性下多主从博弈中均衡的存在性

在已知不确定参数变化范围的假设下,研究多主从博弈中均衡点的存在性问题.基于非合作博弈中NS均衡的定义,提出不确定性下多主从博弈中均衡的概念.基于Fan-Glicksberg不动点定理,证明均衡点的存在性.最后通过算例验证了所提出方法的可行性.     

基于事件驱动控制的混杂动态博弈系统的纳什均衡分析

本文研究了基于事件驱动控制的混杂动态博弈系统的纳什均衡分析问题. 首先, 分析了事件驱动机制对混 杂动态博弈过程的影响, 进而, 在进行状态空间描述的基础上, 给出了混杂动态博弈的纳什均衡的定义, 并建立了对 应博弈系统的策略型模型. 其次, 结合Lanchester方程, 分别讨论了两类混杂动态博弈系统的均衡问题, 包括事件驱 动策略设计和固定的情况, 获得了均衡解存在的必要条件. 最后, 通过数值模拟进行了应用分析, 验证了所取得结果 的合理性和科学性, 并总结了混杂动态博弈研究的未来工作.     

一种基于马尔可夫博弈的能量均衡路由算法

针对无线传感器网络中耗能不均问题,引入马尔可夫博弈理论,构建了无线传感器网络的马尔可夫博弈模型.在能量均衡路由分析的基础上,给出了一种基于马尔可夫博弈的能量均衡路由算法,该算法从无线传感器网络整体耗能出发,兼顾节点之间的合作.定义了能量和信誉值的二元收益函数,给出了节点转发的状态转移概率,根据收益函数进行能量调节,求解出能量和收益之间的均衡系数——纳什均衡,实现了节点能量的均衡消耗,延长了网络的生命周期.使用PRISM概率仿真工具进行仿真,验证了该博弈模型存在纳什均衡点,同时表明该模型能促进节点之间合作,最大化无线传感器网络的生命周期.     

A Monte Carlo Neural Fictitious Self-Play approach to approximate Nash Equilibrium in imperfect-information dynamic games

Solving the optimization problem to approach a Nash Equilibrium point plays an important role in imperfect information games, e.g., StarCraft and poker. Neural Fictitious Self-Play (NFSP) is an effective algorithm that learns approximate Nash Equilibrium of imperfect-information games from purely self-play without prior domain knowledge. However, it needs to train a neural network in an off-policy manner to approximate the action values. For games with large search spaces, the training may suffer from unnecessary exploration and sometimes fails to converge. In this paper, we propose a new Neural Fictitious Self-Play algorithmthat combinesMonte Carlo tree search with NFSP, called MC-NFSP, to improve the performance in real-time zero-sum imperfect-information games. With experiments and empirical analysis, we demonstrate that the proposed MC-NFSP algorithm can approximate Nash Equilibrium in games with large-scale search depth while the NFSP can not. Furthermore, we develop an Asynchronous Neural Fictitious Self-Play framework (ANFSP). It uses asynchronous and parallel architecture to collect game experience and improve both the training efficiency and policy quality. The experiments with th e games with hidden state information (Texas Hold’em), and the FPS (firstperson shooter) games demonstrate effectiveness of our algorithms.     

一种面向多Agent交互的博弈Nash均衡求解方法

现有的图型博弈Nash均衡求解方法基本是在离散化剖面空间中搜索求解，最终只能得到近似Nash均衡。针对现有求解方法存在的不足，把求解图型博弈的Nash均衡看作是连续策略空间中的函数优化问题，定义Agents在策略剖面中的效用偏离度之和为优化目标，其最优解就是博弈的Nash均衡。本文基于对实例的分析指出目标函数下降梯度的计算可归结为一组线性规划，进而提出一种求解图型博弈Nash均衡的新型梯度下降算法。算法分析及实验研究表明，对于多Agent交互模型中的相关问题，本文提出的方法可求解任意图结构图型博弈Nash均衡，对于大规模图型博弈也有较好的求解精度和求解效率。     

Distributed best response dynamics for Nash equilibrium seeking in potential games

In this paper, we consider distributed Nash equilibrium (NE) seeking in potential games over a multi-agent network, where each agent can not observe the actions of all its rivals. Based on the best response dynamics, we design a distributed NE seeking algorithm by incorporating the non-smooth finite-time average tracking dynamics, where each agent only needs to know its own action and exchange information with its neighbours through a communication graph. We give a sufficient condition for the Lipschitz continuity of the best response mapping for potential games, and then prove the convergence of the proposed algorithm based on the Lyapunov theory. Numerical simulations are given to verify the result and illustrate the effectiveness of the algorithm.     

The Nash Equilibrium Point of Dynamic Games Using Evolutionary Algorithms in Linear Dynamics and Quadratic System

In this paper, examining some games, we show that classical techniques are not always effective for games with not many stages and players and it can’t be claimed that these techniques of solution always obtain the optimal and actual Nash equilibrium point. For solving these problems, two evolutionary algorithms are then presented based on the population to solve general dynamic games. The first algorithm is based on the genetic algorithm and we use genetic algorithms to model the players' learning process in several models and evaluate them in terms of their convergence to the Nash Equilibrium. in the second algorithm, a Particle Swarm Intelligence Optimization (PSO) technique is presented to accelerate solutions’ convergence. It is claimed that both techniques can find the actual Nash equilibrium point of the game keeping the problem’s generality and without imposing any limitation on it and without being caught by the local Nash equilibrium point. The results clearly show the benefits of the proposed approach in terms of both the quality of solutions and efficiency.     

Distributed Nash Equilibrium Seeking for General Networked Games With Bounded Disturbances

This paper is concerned with anti-disturbance Nash equilibrium seeking for games with partial information. First, reduced-order disturbance observer-based algorithms are proposed to achieve Nash equilibrium seeking for games with first-order and second-order players, respectively. In the developed algorithms, the observed disturbance values are included in control signals to eliminate the influence of disturbances, based on which a gradient-like optimization method is implemented for each player. Second, a signum function based distributed algorithm is proposed to attenuate disturbances for games with second-order integrator-type players. To be more specific, a signum function is involved in the proposed seeking strategy to dominate disturbances, based on which the feedback of the velocity-like states and the gradients of the functions associated with players achieves stabilization of system dynamics and optimization of players’ objective functions. Through Lyapunov stability analysis, it is proven that the players’ actions can approach a small region around the Nash equilibrium by utilizing disturbance observer-based strategies with appropriate control gains. Moreover, exponential (asymptotic) convergence can be achieved when the signum function based control strategy (with an adaptive control gain) is employed. The performance of the proposed algorithms is tested by utilizing an integrated simulation platform of virtual robot experimentation platform (V-REP) and MATLAB.      

Equilibrium Trajectories in Dynamical Bimatrix Games with Average Integral Payoff Functionals

This paper considers models of evolutionary non-zero-sum games on the infinite time interval. Methods of differential game theory are used for the analysis of game interactions between two groups of participants. We assume that participants in these groups are controlled by signals for the behavior change. The payoffs of coalitions are defined as average integral functionals on the infinite horizon. We pose the design problem of a dynamical Nash equilibrium for the evolutionary game under consideration. The ideas and approaches of non-zero-sum differential games are employed for the determination of the Nash equilibrium solutions. The results derived in this paper involve the dynamic constructions and methods of evolutionary games. Much attention is focused on the formation of the dynamical Nash equilibrium with players strategies that maximize the corresponding payoff functions and have the guaranteed properties according to the minimax approach. An application of the minimax approach for constructing optimal control strategies generates dynamical Nash equilibrium trajectories yielding better results in comparison to static solutions and evolutionary models with the replicator dynamics. Finally, we make a comparison of the dynamical Nash equilibrium trajectories for evolutionary games with the average integral payoff functionals and the trajectories for evolutionary games with the global terminal payoff functionals on the infinite horizon.     

复杂网络上的演化博弈

主要介绍了近年来复杂网络上的演化博弈研究现状和研究方向．复杂网络理论的发展为描述博弈关系提供了系统且方便的框架，网络上的节点表示博弈个体，边代表与其邻居的博弈关系．介绍了经典演化博弈论中的演化稳定策略概念和复制动力学方程，以及二者的相互联系．介绍了混合均匀有限人口中随机演化动力学问题，并给出了与确定复制方程的相互转化关系．介绍了小世界、无标度等复杂网络上演化博弈的研究结论，给出了复杂网络上演化博弈论的未来发展方向．     

New algorithms for approximate Nash equilibria in bimatrix games

We consider the problem of computing additively approximate Nash equilibria in non-cooperative two-player games. We provide a new polynomial time algorithm that achieves an approximation guarantee of 0.36392. We first provide a simpler algorithm, that achieves a 0.38197-approximation, which is exactly the same factor as the algorithm of Daskalakis, Mehta and Papadimitriou. This algorithm is then tuned, improving the approximation error to 0.36392. Our method is relatively fast and simple, as it requires solving only one linear program and it is based on using the solution of an auxiliary zero-sum game as a starting point. Finally we also exhibit a simple reduction that allows us to compute approximate equilibria for multi-player games by using algorithms for two-player games.     

A Class of Evolutionary Models for Participation Games with Negative Feedback

We introduce a framework to analyze the interaction of boundedly rational heterogeneous agents repeatedly playing a participation game with negative feedback. We assume that agents use different behavioral rules prescribing how to play the game conditionally on the outcome of previous rounds. We update the fraction of the population using each rule by means of a general class of evolutionary dynamics based on imitation, which contains both replicator and logit dynamics. Our model is analyzed by a combination of formal analysis and numerical simulations and is able to replicate results from the experimental and computational literature on these types of games. In particular, irrespective of the specific evolutionary dynamics and of the exact behavioral rules used, the dynamics of the aggregate participation rate is consistent with the symmetric mixed strategy Nash equilibrium, whereas individual behavior clearly departs from it. Moreover, as the number of players or speed of adjustment increase the evolutionary dynamics typically becomes unstable and leads to endogenous fluctuations around the steady state. These fluctuations are robust with respect to behavioral rules that try to exploit them.