Deep reinforcement learning-based cooperative interactions among heterogeneous vehicular networks

Most real-world vehicle nodes can be structured into an interconnected network of vehicles. Through structuring these services and vehicle device interactions into multiple types, such internet of vehicles becomes multidimensional heterogeneous overlay networks. The heterogeneousness of the overlays makes it difficult for the overlay networks to coordinate with each other to improve their performance. Therefore, it poses an interesting but critical challenge to the effective analysis of heterogeneous virtual vehicular networks. A variety of virtual vehicular networks can be easily deployed onto the native network by applying the concept of SDN (Software Defined Networking). These virtual networks reflect their heterogeneousness due to their different performance goals, and they compete for the same physical resources of the underlying network, so that a sub-optimal performance of the virtual networks may be achieved. Therefore, we propose a Deep Reinforcement Learning (DRL) approach to make the virtual networks cooperate with each other through the SDN controller. A cooperative solution based on the asymmetric Nash bargaining is proposed for co-existing virtual networks to improve their performance. Moreover, the Markov Chain model and DRL resolution are introduced to leverage the heterogeneous performance goals of virtual networks. The implementation of the approach is introduced, and simulation results confirm the performance improvement of the latency sensitive, loss-rate sensitive and throughput sensitive heterogeneous vehicular networks using our cooperative solution.     

Mixed coordination mechanisms for scheduling games on hierarchical machines

In this paper, we study scheduling games under mixed coordination mechanisms on hierarchical machines. The two scheduling policies involved are ‐ and ‐, where ‐ (resp., ‐) policy sequences jobs in nondecreasing order of their hierarchies, and jobs of the same hierarchy in nonincreasing (resp., nondecreasing) order of their processing times. We first show the existence of a Nash equilibrium. Then we present the price of anarchy and the price of stability for the games with social costs of minimizing the makespan and maximizing the minimum machine load. All the bounds given in this paper are tight.     

不完全信息下的最终报价仲裁

基于争议双地仲裁人的裁决值有不同的概率估计，而这些估计是双方共同知识这一前提分析最终报价中仲裁，把它构造成一个不完全信息的非零和对策模型。然后给出了局部、全局Ｎａｓｈ平衡报价策略存在唯一的充分条件，最后探讨了双方认识存在的差异对争议人报价行为的影响。     

会计信息失真现象的博弈分析

在对现实进行合理假定的基础上建立了会计信息失真现象的有限完美信息动态博弈模型，并利用后退归纳法找到了五个满足不同条件的纳什均衡解。通过对均衡条件的分析，找出了导致会计信息失真的治理结构原因和法律制度因素，并提出了建设性的解决措施。     

A constructive approach to sequential Nash equilibria

We present a Coq-formalized proof that all non-cooperative, sequential games have a Nash equilibrium point. Our proof methodology follows the style advocated by LCF-style theorem provers, i.e., it is based on inductive definitions and is computational in nature. The proof (i) uses simple computational means, only, (ii) basically is by construction, and (iii) reaches a constructively stronger conclusion than informal efforts. We believe the development is a first as far as formalized game theory goes.     

证券市场中庄家与散户间的确定性微分博弈

基于确定性微分博弈理论,建立了一种庄家与散户间的连续时间的博弈模型.首先将所有散户作为一个整体与庄家进行博弈,以博弈双方持股率的动态关系作为动态系统方程,并以此构建了一个确定性微分博弈模型; 然后运用开环纳什均衡和反馈纳什均衡分别求解出满足共态函数的常微分方程组和满足价值函数的Issacs - Bellman偏微分方程,以此得到庄家与散户博弈的开环纳什均衡策略和反馈纳什均衡策略.该结果可为金融监管部门监管证券市场和证券市场投资者买卖股票提供参考.     

基于谈判集的模糊合作博弈的收益分配方案

为了弥补核心作为博弈解可能为空集的缺陷,将经典合作博弈中的谈判集解拓展到模糊合作博弈中,提出了模糊合作博弈的模糊谈判集的概念.探讨模糊网络博弈的模糊谈判集和核心的等价性质,并对相关结论加以证明.研究表明,模糊合作博弈的模糊谈判集是经典合作博弈谈判集的自然拓展,该结果丰富了对模糊合作博弈的解的研究,也表明了模糊网络博弈核心的非空性,进而保证其最优分配方案的存在性.     

基于Q学习的DDoS攻防博弈模型研究

新形势下的DDoS攻防博弈过程和以往不同,因此利用现有的方法无法有效地评估量化攻防双方的收益以及动态调整博弈策略以实现收益最大化。针对这一问题,设计了一种基于Q学习的DDoS攻防博弈模型,并在此基础上提出了模型算法。首先,通过网络熵评估量化方法计算攻防双方收益;其次,利用矩阵博弈研究单个DDoS攻击阶段的攻防博弈过程;最后,将Q学习引入博弈过程,提出了模型算法,用以根据学习效果动态调整攻防策略从而实现收益最大化。实验结果表明,采用模型算法的防御方能够获得更高的收益,从而证明了算法的可用性和有效性。     

几何过程与最优保修策略

研究了几何过程的特性,举例说明了它在最优保修策略中的应用,并对增加谈判力提出几点合理建议。     

基于博弈论的协作超宽带系统资源分配算法

为使多用户协作超宽带系统的资源分配算法能在有效利用系统资源的同时满足用户的服务质量QoS（Quality of Service）公平性需求,在纳什议价解方法和凸优化理论基础上,将合作博弈论方法用于协作多频带超宽带系统的资源优化分配中。提出以最大化系统净效用为目标,以用户的QoS需求为公平性指标的协作超宽带系统协作伙伴选择算法和自适应功率分配算法。通过仿真与最大化系统速率（max-rate）和最大化最小用户速率（max-min）公平性算法作比较,证明了该资源分配方法在最大化系统速率和用户QoS公平性两方面有很好的折衷,适于超宽带系统。