Hierarchical hybrid control network design based on LON and master-slave RS-422/485 protocol

Aiming at the weaknesses of LON bus, combining the coexistence of fieldbus and DCS (Distribu ted Control Systems) in control networks, the authors introduce a hierarchical hybrid control network design based on LON and master slave RS 422/485 protocol. This design adopts LON as the trunk, master slave RS 422/485 control networks are connected to LON as special subnets by dedicated gateways. It is an implementation method for isomerous control network integration. Data management is ranked according to real time requirements for different network data. The core components, such as control network nodes, router and gateway, are detailed in the paper. The design utilizes both communication advantage of LonWorks technology and the more powerful control ability of universal MCUs or PLCs, thus it greatly increases system response speed and performance cost ratio.

A Game Theoretic Approach for Hierarchical Caching Resource Sharing in 5G Networks with Virtualization

Caching and virtualization have been considered as the promising techniques in 5G Networks. In 5G networks with virtualization, the caching resources deployed by infrastructure providers (InPs) can be abstracted into isolated slices and transparently shared by mobile virtual network operators (MVNOs). In this case, one of the most important issues is how the MVNOs to share the caching resource. To solve this issue, different from previous works, a hierarchical caching architecture that core network and radio access network (RAN) have the caching capability in 5G networks with virtualization is first considered in this paper. Then, we study the problem of hierarchical caching resource sharing for MVNOs, and a competitive game to maximize their expectation revenue based on the oligopoly market model is formulated. As it is a hard problem to find the optimal solution in the hierarchical caching resource sharing problem, we decompose the optimization problem into two independent caching resource sharing problems in RAN and core network, respectively. Then the local optimal solutions are solved and the global Nash equilibrium solution is achieved. Finally, simulation results are illustrated to demonstrate the performance of the proposed scheme.

Game-theoretic approach to power and admission control in hierarchical wireless sensor networks

Power efficiency and link reliability are of great impor- tance in hierarchical wireless sensor networks （HWSNs）, espe- cially at the key level, which consists of sensor nodes located only one hop away from the sink node called OHS. The power and admission control problem in HWSNs is comsidered to improve its power efficiency and link reliability. This problem is modeled as a non-cooperative game in which the active OHSs are con- sidered as players. By applying a double-pricing scheme in the definition of OHSs＇ utility function, a Nash Equilibrium solution with network properties is derived. Besides, a distributed algorithm is also proposed to show the dynamic processes to achieve Nash Equilibrium. Finally, the simulation results demonstrate the effec- tiveness of the proposed algorithm.

Strategic games on a hierarchical network model

Among complex network models,the hierarchical network model is the one most close to such real networks as world trade web,metabolic network,WWW,actor network,and so on.It has not only the property of power-law degree distribution,but also the scaling clustering coefficient property which Barabási-Albert(BA)model does not have.BA model is a model of network growth based on growth and preferential attachment,showing the scale-free degree distribution property.In this paper,we study the evolution of cooperation on a hierarchical network model,adopting the prisoner's dilemma(PD)game and snowdrift game(SG)as metaphors of the interplay between connected nodes.BA model provides a unifying framework for the emergence of cooperation.But interestingly,we found that on hierarchical model,there is no sign of cooperation for PD game,while the fre-quency of cooperation decreases as the common benefit decreases for SG.By comparing the scaling clustering coefficient prop-erties of the hierarchical network model with that of BA model,we found that the former amplifies the effect of hubs.Considering different performances of PD game and SG on complex network,we also found that common benefit leads to cooperation in the evolution.Thus our study may shed light on the emergence of cooperation in both natural and social environments.

Hierarchical Control Strategy for Load Regulation Based on Stackelberg Game Theory Considering Randomness

Demand response has been recognized as a valuable functionality of power systems for mitigating power imbalances.This paper proposes a hierarchical control strategy among the distribution system operator(DSO),load aggregators(LAs),and thermostatically controlled loads(TCLs);the strategy includes a scheduling layer and an executive layer to provide load regulation.In the scheduling layer,the DSO(leader)offers compensation price(CP)strategies,and the LAs(followers)respond to CP strategies with available regulation power(ARP)strategies.Profits of the DSO and LAs are modeled according to their behaviors during the load regulation process.Stackelberg game is adopted to capture interactions among the players and leader and to obtain the optimal strategy for each participant to achieve utility.Moreover,considering inevitable random factors in practice,e.g.,renewable generation and behavior of users,two different stochastic models based on sample average approximation(SAA)and parameter modification are formulated with improved scheduling accuracy.In the executive layer,distributed TCLs are triggered based on strategies determined in the scheduling layer.A self-triggering method that does not violate user privacy is presented,where TCLs receive external signals from the LA and independently determine whether to alter their operation statuses.Numerical simulations are performed on the modified IEEE-24 bus system to verify effectiveness of the proposed strategy.

Study on group air to ground attack-defends hierarchical dynamic decision-making

As to oppositional, multi-objective and hierarchical characteristic of air formation to ground attackdefends campaign, and using dynamic space state model of military campaign, this article establishes a principal and subordinate hierarchical interactive decision-making way, the Nash-Stackelberg-Nash model, to solve the problems in military operation, and find out the associated best strategy in hierarchical dynamic decision-making. The simulating result indicate that when applying the model to air formation to ground attack-defends decision-making system, it can solve the problems of two hierarchies, dynamic oppositional decision-making favorably, and reach preferable effect in battle. It proves that the model can provide an effective way for analyzing a battle,

Research on the bi-layer low carbon optimization strategy of integrated energy system based on Stackelberg master slave game

With increasing reforms related to integrated energy systems(IESs),each energy subsystem,as a participant based on bounded rationality,significantly influences the optimal scheduling of the entire IES through mutual learning and imitation.A reasonable multiagent joint operation strategy can help this system meet its low-carbon objectives.This paper proposes a bilayer low-carbon optimal operational strategy for an IES based on the Stackelberg master-slave game and multiagent joint operation.The studied IES includes cogeneration,power-to-gas,and carbon capture systems.Based on the Stackelberg master-slave game theory,sellers are used as leaders in the upper layer to set the prices of electricity and heat,while energy producers,energy storage providers,and load aggregators are used as followers in the lower layer to adjust the operational strategy of the system.An IES bilayer optimization model based on the Stackelberg master-slave game was developed.Finally,the Karush-Kuhn-Tucker(KKT)condition and linear relaxation technology are used to convert the bilayer game model to a single layer.CPLEX,which is a mathematical program solver,is used to solve the equilibrium problem and the carbon emission trading cost of the system when the benefits of each subject reach maximum and to analyze the impact of different carbon emission trading prices and growth rates on the operational strategy of the system.As an experimental demonstration,we simulated an IES coupled with an IEEE 39-node electrical grid system,a six-node heat network system,and a six-node gas network system.The simulation results confirm the effectiveness and feasibility of the proposed model.

基于演员-评论家框架的层次化多智能体协同决策方法

针对复杂作战环境下多智能体协同决策中出现的任务分配不合理、决策一致性较差等问题,提出一种基于演员-评论家(Actor-Critic,AC)框架的层次化多智能体协同决策方法。通过将决策过程分为不同层次,并使用AC框架来实现智能体之间的信息交流和决策协同,以提高决策效率和战斗力。在高层次,顶层智能体制定任务决策,将总任务分解并分配给底层智能体。在低层次,底层智能体根据子任务进行动作决策,并将结果反馈给高层次。实验结果表明,所提方法在多种作战仿真场景下均取得了较好的性能,展现了其在提升军事作战协同决策能力方面的潜力。

基于改进分层博弈交叉效率模型的政策评价

政策评估作为政策实施过程中的关键环节,是检验政策效果的基本途径与实现资源优化配置的重要依据.在复杂博弈情境下,决策单元群体间竞合关系不变的假设已经不能满足现实中效率评价的需求,考虑群体间动态变化的合作与竞争关系至关重要.本文将外界冲击下主体间竞合关系演变纳入效率评价框架,提出改进分层博弈交叉数据包络分析方法:从公平的视角出发,基于二次目标模型构建多层次效率矩阵,结合Shapley值对决策单元动态关系演变进行效率评价,进而,以水权交易市场试点政策为例,验证该模型的可行性.该方法放松了政策前后群体竞合关系稳定的假设,验证了水权交易市场政策的有效性和外溢性,为能源与环境领域试点政策效果评价提供了新方法.

基于远程医疗的“互联网+”分级诊疗合作策略研究

[目的]探讨基于远程医疗的“互联网+”分级诊疗利益分配和推广策略,为我国远程医疗合理定价和有效运营提供参考。[方法]对“互联网+”分级诊疗系统进行抽象后构建社区医院-大医院的博弈模型,并进行均衡求解。[结果]当远程医疗与线下的相对价格较高时双方更可能合作;而当双方利益分配比例差距较大时双方则更可能放弃合作。社区医院对患者健康结果关注度越大,远程医疗利用率越高,混合策略均衡中双方的合作概率越大。[结论]基于远程医疗的“互联网+”分级诊疗具有推广的潜力,应适当提升远程医疗定价,平衡两级医院的利益分配机制,并在合作过程中给予监管和引导。

基于分层强化学习的多智能体博弈策略生成方法

典型基于深度强化学习的多智能体对抗策略生成方法采用“分总”框架,各智能体基于部分可观测信息生成策略并进行决策,缺乏从整体角度生成对抗策略的能力,大大限制了决策能力。为了解决该问题,基于分层强化学习提出改进的多智能体博弈策略生成方法。基于分层强化学习构建观测信息到整体价值的决策映射,以最大化整体价值作为目标构建优化问题,并推导了策略优化过程,为后续框架结构和方法实现的设计提供了理论依据;基于决策映射与优化问题构建,采用神经网络设计了模型框架,详细阐述了顶层策略控制模型和个体策略执行模型;基于策略优化方法,给出详细训练流程和算法流程;采用星际争霸多智能体对抗(StarCraft Multi-Agent Challenge,SMAC)环境,与典型多智能体方法进行性能对比。实验结果表明,该方法能够有效生成对抗策略,控制异构多智能体战胜预设对手策略,相比典型多智能体强化学习方法性能提升明显。

静态和分级惩罚机制下短视频平台监管策略的演化博弈研究

为了破解短视频市场的监管困局,构建行而有效的短视频平台监管机制,基于演化博弈理论,分别构建了政府静态惩罚机制和分级惩罚机制下短视频平台和用户的演化博弈模型,分析了双方行为决策的动态演化路径和相互作用机理。研究表明:静态惩罚机制下,当政府惩罚力度或平台声誉损失逐渐增加时,短视频平台倾向于选择“严格监管”策略,但随着监管成本与流失用户成本的增加,短视频平台继而转向选择“消极监管”策略。用户的策略选择取决于平台惩罚和政府惩罚,当且仅当平台惩罚与政府惩罚均较大时,用户才会选择“合规”策略。分级惩罚机制下,增大一级惩罚与二级惩罚的力度,也将使平台选择“严格监管”策略。分级惩罚机制下短视频平台严格监管的概率随着用户合规概率的升高而降低,而静态惩罚机制与之相反。更为重要的是,相比于静态惩罚机制,分级惩罚机制下短视频平台总是会选择“严格监管”策略。

海空跨域协同兵棋AI架构设计及关键技术分析

以深度强化学习为核心的智能博弈技术在游戏领域内的突破和进展为海空兵棋AI的研究提供了借鉴。智能体架构设计是需要解决的关键问题,良好的架构能够降低算法训练的复杂度和难度,加快策略收敛。提出基于随机博弈的海空跨域协同决策博弈模型,分析了相关的均衡解概念;在分析典型智能体框架基础上,针对海空兵棋推演决策博弈过程,提出基于多智能体分层强化学习的智能体双层架构,能够有效解决智能体间协作和维度灾难问题;从兵力协同、智能体网络设计、对手建模和训练机制共4个方面分析了关键技术。期望为海空兵棋AI设计实现提供架构指导。

基于学习博弈和契约论的分层联邦学习隐私保护激励机制

分层联邦学习(hierarchical federated learning,HFL)旨在通过多层架构的协作学习,同时保护隐私和优化模型性能.但其效果需依赖于针对参与各方的有效激励机制及应对信息不对称的策略.为了解决上述问题,本文提出一种保护终端设备、边缘服务器及云服务器隐私的分层激励机制.在边端层,边缘服务器作为中介应用多维合约理论设计不同类型的契约项,促使终端设备在不泄露数据采集、模型训练以及模型传输成本的情况下,使用本地数据参与HFL.在云边层,云服务器与边缘服务器间关于单位数据奖励和数据量的关系通过Stackelberg博弈进行建模,在不泄露边缘服务器单位利润的情况下,进一步将其转化为马尔可夫过程,并采用保护隐私的多智能体深度强化学习(multi-agent deep reinforcement learning,MADRL)方法逐渐接近斯塔克伯格均衡(Stackelberg equilibrium,SE).实验结果表明,本文提出的分层激励机制在性能上优于基线方法,云服务器的收益提升了接近11%,单位成本获取增益提升接近18倍.

价值共创驱动新质公安战斗力发展——多方合作的博弈分析

[目的/意义]为了加速新质公安战斗力发展进程,推动公安大数据平台数据的持续融合共享,消除各警种、部门形成的数据壁垒,探究传统科层组织结构对公安大数据平台价值共创实现的影响具有重要意义。[方法/过程]基于演化博弈理论,构建公安大数据平台价值共创演化博弈模型,分析数据融合方、数据传递方、数据支撑方在传统科层组织因素影响下价值共创策略演化过程和走向,并运用Matlab2023b进行仿真分析。[结果/结论]数据融合方策略选择受到公安机关“条块结合”领导体制的影响,应当借助平台的互动关系实现公安机关组织机制转型,削弱传统科层体制的主导作用,实现数据共享的良性动态循环。

基于层次逻辑博弈Petri网地铁应急管理系统建模与分析

为协调地铁应急管理系统中各方利益关系,提高应急管理效率,提出层次逻辑博弈Petri网.提出层次模型构建方法,引入替代变迁代替子网,在子网中定义相应入口库所和出口库所;提出替代变迁启用算法、新可达图生成算法和效用矩阵分析方法;基于层次逻辑博弈Petri网,构建地铁应急管理系统的整体博弈关系模型,以应急决策机构和公众博弈子网为例,分析混合策略博弈过程,验证模型的正确性和可行性.

基于主从博弈的分层联邦学习激励机制研究

为了优化分层联邦学习(FL)全局模型的训练时延,针对实际场景中终端设备存在自私性的问题,该文提出一种基于博弈论的激励机制。在激励预算有限的条件下,得到了终端设备和边缘服务器之间的均衡解和最小的边缘模型训练时延。考虑终端设备数量不同,设计了基于主从博弈的可变激励训练加速算法,使得一次全局模型训练时延达到最小。仿真结果显示,所提出的算法能够有效降低终端设备自私性带来的影响,提高分层联邦学习全局模型的训练速度。

基于Nash-Stackelberg分层博弈模型的路网交通控制强化学习算法

为了解决多交叉口博弈引发的Nash均衡计算复杂度问题,考虑路网中不同交叉口的重要程度和博弈关系,兼顾路网中子区之间及子区内部的交通控制策略,以2个子区内的重要交叉口作为上层博弈主体,次要交叉口作为下层博弈主体,构建了一种Nash-Stackelberg分层博弈(NSHG)模型.然后,提出2种多Agent强化学习算法,即基于NSHG的Q学习(NSHG-QL)算法和基于NSHG的深度Q网络(NSHG-DQN)算法.在实验中,使用NSHG-QL和NSHG-DQN算法在SUMO仿真软件搭建的路网环境中对信号灯进行控制,并与基础博弈模型求解算法进行比较.实验结果表明:NSHG-QL算法和NSHG-DQN算法减少了交叉口内车辆的平均旅行时间和平均时间损失,提高了平均速度;NSHG模型在满足重要交叉口间上层博弈的基础上协调次要交叉口,做出最优策略选择,而且基于分层博弈模型的多Agent强化学习算法能明显提高学习性能和收敛性.

基于多领导者Stackelberg博弈的分层联邦学习激励机制设计

分层联邦学习中隐私安全与资源消耗等问题的存在降低了参与者的积极性。为鼓励足够多的参与者积极参与学习任务,并针对多移动设备与多边缘服务器之间的决策问题,提出基于多领导者Stackelberg博弈的激励机制。首先,通过量化移动设备的成本效用与边缘服务器的支付报酬,构建效用函数并定义最优化问题;其次,将移动设备之间的交互建模为演化博弈,将边缘服务器之间的交互建模为非合作博弈。为求解最优边缘服务器选择和定价策略,提出多轮迭代边缘服务器选择算法(MIES)和梯度迭代定价算法(GIPA),前者用于求解移动设备之间的演化博弈均衡解,后者用于求解边缘服务器之间的定价竞争问题。实验结果表明,所提算法GIPA与最优定价预测策略(OPPS)、历史最优定价策略(HOPS)和随机定价策略(RPS)相比,可使边缘服务器的平均效用分别提高4.06%、10.08%和31.39%。

基于博弈论组合赋权和云模型的高校科技成果转化绩效评价研究

高校科技成果转化是我国高质量发展的重要新引擎,对其进行绩效评价的重要性日益凸显。面对评价过程中出现的不确定信息,引入云模型,提出了一种基于博弈论组合赋权和云模型的高校科技成果转化绩效评价模型,并采用IKLCM(improved Kullback-Leibler divergence based on cloud model)法计算云相似度距离,得出评价结果。通过对西南地区5个省份的高校进行实证分析,结果表明,该模型综合考虑主客观因素,组合权重科学合理,绩效云模型评价能得到准确可靠的评价结果。