V2V Energy Trading Considering User Satisfaction under Low-Carbon Objectives via Bayesian Game

In response to the additional load impact caused by the integration of electric vehicles (EVs) into the grid or microgrids (MGs), as well as the issue of low responsiveness of EV users during vehicle-to-vehicle (V2V) power exchange processes, this paper explores a multi-party energy trading model considering user responsiveness under low carbon goals. The model takes into account the stochastic charging and discharging characteristics of EVs, user satisfaction, and energy exchange costs, and formulates utility functions for participating entities. This transforms the competition in multi-party energy trading into a Bayesian game problem, which is subsequently resolved. Furthermore, this paper primarily employs sensitivity analysis to evaluate the impact of multi-party energy trading on user responsiveness and green energy utilization, with the aim of promoting incentives in the electricity trading market and aligning with low-carbon requirements. Finally, through case simulations, the effectiveness of this model for the considered scenarios is demonstrated.

Clustering routing algorithm of wireless sensor networks based on Bayesian game

To avoid uneven energy consuming in wireless sen- sor networks, a clustering routing model is proposed based on a Bayesian game. In the model, Harsanyi transformation is introduced to convert a static game of incomplete information to the static game of complete but imperfect information. In addition, the existence of Bayesian nash equilibrium is proved. A clustering routing algorithm is also designed according to the proposed model, both cluster head distribution and residual energy are considered in the design of the algorithm. Simulation results show that the algorithm can balance network load, save energy and prolong network lifetime effectively.

Using Bayesian Game Model for Intrusion Detection in Wireless Ad Hoc Networks

Wireless ad ho network is becoming a new research fronter, in which security is an important issue. Usually some nodes act maliciously and they are able to do different kinds of Denial of Service (Dos). Because of the limited resource, intrusion detection system (IDS) runs all the time to detect intrusion of the attacker which is a costly overhead. In our model, we use game theory to model the interactions between the intrusion detection system and the attacker, and a realistic model is given by using Bayesian game. We solve the game by finding the Bayesian Nash equilibrium. The results of our analysis show that the IDS could work intermittently without compromising on its effectiveness. At the end of this paper, we provide an experiment to verify the rationality and effectiveness of the proposed model.

One-Time Rational Secret Sharing Scheme Based on Bayesian Game

The rational secret sharing cannot be realized in the case of being played only once, and some punishments in the one-time rational secret sharing schemes turn out to be empty threats. In this paper, after modeling 2-out-of-2 rational secret sharing based on Bayesian game and considering different classes of protocol parties, we propose a 2-out-of-2 secret sharing scheme to solve cooperative problem of a rational secret sharing scheme being played only once. Moreover, we prove that the strategy is a perfect Bayesian equilibrium, adopted only by the parties in their decision-making according to their belief system （denoted by the probability distribution） and Bayes rule, without requiring simultaneous channels.

A Bayesian Game Approach for Demand Response Management Considering Incomplete Information

Residential flexible resource is attracting much attention in demand response(DR)for peak load shifting.This paper proposes a scenario for multi-stage DR project to schedule energy consumption of residential communities considering the incomplete information.Communities in the scenario can decide whether to participate in DR in each stage,but the decision is the private information that is unknown to other communities.To optimize the energy consumption,a Bayesian game approach is formulated,in which the probability characteristic of the decision-making of residential communities is described with Markov chain considering human behavior of bounded rationality.Simulation results show that the proposed approach can benefit all residential communities and power grid,but the optimization effect is slightly inferior to that in complete information game approach.

Learning implicit information in Bayesian games with knowledge transfer

In this paper,we consider to learn the inherent probability distribution of types via knowledge transfer in a two-player repeated Bayesian game,which is a basic model in network security.In the Bayesian game,the attacker's distribution of types is unknown by the defender and the defender aims to reconstruct the distribution with historical actions.lt is dificult to calculate the distribution of types directly since the distribution is coupled with a prediction function of the attacker in the game model.Thus,we seek help from an interrelated complete-information game,based on the idea of transfer learning.We provide two different methods to estimate the prediction function in difftrent concrete conditions with knowledge transfer.After obtaining the estimated prediction function,the deiender can decouple the inherent distribution and the prediction function in the Bayesian game,and moreover,reconstruct the distribution of the attacker's types.Finally,we give numerical examples to illustrate the effectiveness of our methods.

HYPERGAMES AND BAYESIAN GAMES:A THEORETICAL COMPARISON OF THE MODELS OF GAMES WITH INCOMPLETE INFORMATION

The present study discusses the relationships between two independently developed models of games with incomplete information, hypergames （Bennett, 1977） and Bayesian games （Harsanyi, 1967）. The authors first show that any hypergame can naturally be reformulated in terms of Bayesian games in an unified way. The transformation procedure is called Bayesian representation of hypergame. The authors then prove that some equilibrium concepts defined for hypergames are in a sense equivalent to those for Bayesian games. Furthermore, the authors discuss carefully based on the proposed analysis how each model should be used according to the analyzer＇s purpose.

Hierarchical Hypergames and Bayesian Games:A Generalization of the Theoretical Comparison of Hypergames and Bayesian Games Considering Hierarchy of Perceptions

This paper discusses the relationship of two independently developed models of games with incomplete information,hierarchical hypergames and Bayesian games.It can be considered as a generalization of the previous study on the theoretical comparison of simple hypergames and Bayesian games(Sasaki and Kijima,2012) by taking into account hierarchy of perceptions,i.e.,an agent's perception about the other agents' perceptions,and so on.The authors first introduce the general way of transformation of any hierarchical hypergames into corresponding Bayesian games,which was called as the Bayesian representation of hierarchical hypergames.The authors then show that some equilibrium concepts for hierarchical hypergames can be associated with those for Bayesian games and discuss implications of the results.

The impact of honesty and trickery on a Bayesian quantum prisoners’ dilemma game

To explore the influence of quantum information on the common social problem of honesty and trickery,we propose a Bayesian model for the quantum prisoners’dilemma game.In this model,the players’strategy formation is regarded as a negotiation of their move contract based on their types of decision policies,honesty or trickery.Although the implementation of quantum information cannot eliminate tricky players,players in our model can always end up with higher payoffs than in the classical game.For a good proportion of a credibility parameter value,a rational player will take an honest action,which is in remarkable contrast to the observation that players tend to defect in the classical prisoners’dilemma game.This research suggests that honesty will be promoted to enhance cooperation with the assistance of quantum information resources.

IoT Security Situational Awareness Based on Q-Learning and Bayesian Game

IoT security is very crucial to IoT applications,and security situational awareness can assess the overall security status of the IoT.Traditional situational awareness methods only consider the unilateral impact of attack or defense,but lackconsideration of joint actions by both parties.Applying gametheory to security situational awareness can measure the impact of the opposition and interdependence of the offensive and defensive parties.This paper proposes an IoT security situational awareness method based on Q-Learning and Bayesian game.Through Q-Learning update,the long-term benefits of action strategies in specific states were obtained,and static Bayesian game methods were used to solve the Bayesian Nash Equilibrium of participants of different types.The proposed method comprehensively considers offensive and defensive actions,obtains optimal defense decisions in multi-state and multi-type situations,and evaluates security situation.Experimental results prove the effectiveness of this method.

基于不完全信息静态博弈的工控系统风险评估方法

针对目前大多数工业控制系统风险评估方法未思考防御者策略以及攻防两者之间的对抗问题,提出一种基于博弈模型的风险评估方法。通过攻击防御图模型,计算攻击收益和防御收益;建立静态贝叶斯攻防博弈模型,计算混合策略贝叶斯纳什均衡,获得攻防两者最优策略概率分布。根据信息安全风险评估的计算方法,以防御者收益和攻击者最优策略选择概率分布为基础进行风险评估计算。通过一个实例证明了该方法的可行性和有用性。

生态服务价值与社会经济因素交互影响下的流域生态补偿标准研究

流域生态补偿是实现流域环境改善与区域经济均衡发展的一种政策工具,对消除上下游地区间的经济差异影响,实现区域社会发展的公平性具有重要意义。为定量寻求多元信息扰动下的流域生态补偿标准最优解,亟需开展基于多维关系协调及经济利益分配多目标优化求解的动态均衡博弈研究。以实现生态补偿标准讨价还价贝叶斯纳什均衡为目标,借助完全信息博弈的混合均衡策略实施过程探讨流域生态补偿标准均衡解存在条件,并基于流域上下游间的动态转移支付进行生态补偿标准均衡解求解,依托激励相容机制开展讨价还价的动态博弈贝叶斯均衡的合理性分析。本文结合弥河流域经济社会取用水费用标准及水资源使用价值的变化规律,在流域生态保护和社会经济发展基本均衡的情况下,下游的寿光市、中游的青州市对上游临朐县的水量-水质生态补偿额度分别为3194万元、859万元。本文给出的研究方法可用于处理博弈过程中的冗余信息以便于进行讨价还价,形成合理的流域生态补偿标准。

基于不完全信息的工业园区多主体需求响应博弈策略研究

随着我国电力市场化改革进程的推进与发展,在工业园区层面如何攫取各响应主体最大利润,以引导用户积极参与需求响应成为亟待解决的新问题。基于响应流程中市场信息披露有限且用户与聚合商自由缔结合约的情形,本文提出了一种聚合商视角下的最佳博弈策略及针对用户的收益分配方式。考虑工业园区内用户的利益关系,建立以不完全信息贝叶斯博弈作为上层、以合作博弈作为下层的多主体双层博弈架构;其次以最大化自身利益为目标,采用改进的虚拟遗憾最小化思想结合启发式算法求解博弈均衡,达成园区内的稳定合作关系。最后通过算例进行验证,分别设置了不同场景下申报策略和收益分配两方面的多个对比模型,结果表明本文所提方法得出的策略能够提高用户收益,且利益分配机制的合理选择能够在提高用户需求响应积极性方面起到促进作用。

交互博弈引导的网络流量异常检测建模方法研究

基于网络流量的系统入侵会带来严重破坏,因此寻找能够准确识别和分类异常流量的方法具有重要的研究价值。数据作为基于机器学习模型的检测算法的唯一依据,训练过程对于外界是一个黑盒过程,整个模型在训练和使用过程中缺乏用户交互。这导致在网络运维场景中,专业运维人员不能根据当前模型检测结果,实时将指导信息反馈到系统中,进而削弱了系统的场景适应能力和检测纠错能力。本文基于强化学习过程,设计了一种基于动态贝叶斯博弈的交互引导式的网络流量异常检测方法。通过检测模型和运维人员交互的方式,在训练过程中让运维人员提供专业反馈使得模型获得外界针对当前检测效果的奖惩信号,从而对自身特征聚焦方向和收敛过程起到引导的作用。将运维人员和检测模型视为博弈的双方,建立博弈模型,使双方之间的交互引导行为达到动态平衡状态。通过博弈对于模型交互频次和内容反馈给出指导,从而使得模型具有动态适应当前场景的能力,有效控制了人机交互反馈所带来的系统开销。实验部分验证了交互式博弈的流量检测方法中,双方博弈指导交互行为的可行性与有效性,证明了该方法在动态场景中具有良好的适应能力。相较于传统的机器学习方法,交互引导式模型提高了模型整体的检测性能。性能对比测试结果表明交互频次每增加0.02%,系统整体检测性能随之提升0.01%。

基于博弈论-模糊贝叶斯网络的低温风洞风险评估研究

为对低温风洞可能存在的风险进行识别和评估,本文基于贝叶斯网络方法,首先对低温风洞风险因素进行识别,得出可能导致低温风洞事故的风险因素,依据三角模糊数和博弈论的方法计算各因素发生模糊概率,以此来构建低温风洞风险因素贝叶斯网络模型。通过该贝叶斯网络模型的正向推理和逆向推理对各风险因素进行因果推理和风险诊断,并将其应用到某低温风洞系统。研究结果表明:B_(2)(冻伤、窒息)、B_(3)(机械伤害)、B_(5)(触电)发生概率相对较大,应加大对应风险因素的预防工作;C_(18)(违章作业)、C_(9)(人员误操作)、C_(2)(设备设计缺陷)、C_(1)(设备部件损坏、老化)在低温风洞后验概率排序表中排序较为靠前,在风险事故发生时应该重点进行排查,并采取有效的风险管控措施。研究结果可为低温风洞风险评估及安全决策和方案优化提供一定的参考。

基于贝叶斯攻击图的RFID系统安全博弈分析模型

针对RFID系统缺乏综合有效的风险管理与安全评估的问题,为了实现对RFID系统安全风险的有效分析以及对目标RFID系统整体风险状况的评估,本文提出一种基于贝叶斯攻击图的RFID系统安全博弈分析方法。在贝叶斯攻击图模型的基础上结合博弈思想对RFID系统的风险状况进行分析,将攻击者入侵系统的过程抽象为攻防双方的博弈模型。首先依据目标系统的相关信息确定攻防策略,并且通过对攻击者和防御者策略收益的计算,构建相应的攻防博弈矩阵,然后得出其纳什均衡状态,确定各参与者的最优策略,最后计算双方的期望收益,确定目标RFID系统的安全状态:若是攻击者期望收益大于防御者期望收益,则系统处于风险状态,反之系统则处于安全状态。实验结果表明,本文提出的博弈模型可以良好地实现对目标RFID系统的安全状况分析。

基于分布式控制的多无人机协作喷洒技术

针对现有的多无人机协同中航迹规划难、造成喷洒重叠遗漏等问题,文中研究了一种优化航迹的多无人机协同精准喷洒关键技术。建立四旋翼植保无人机的动力学模型,进行动态规划算法下的模拟实验。将无人机的作业能耗和非作业能耗、药液容量、路径长度作为约束条件,尽量减少转弯次数来优化航迹。该无人机集群采用分布式结构,利用了基于节点类型的贝叶斯联盟博弈算法,来对无人机联盟结构进行动态调整,达到航迹的优化目的。该方法已成功地应用于多无人机协同精准喷洒作业中,实验验证了该方法对航迹优化的有效性。

基于不完全信息博弈的云制造群智能优化方法

为解决云制造过程中云平台经营方与需求方之间的不完全信息以及相互竞争制约的关系导致制造服务难以抉择的问题,提出了一种基于不完全信息博弈模型的云制造群智能优化方法。以各自理性追求自身收益函数最大化为目标,针对需求方与云平台之间的利益竞争关系建立了基于不完全信息的静态博弈模型,并提出了需求方与云平台之间的竞争规则,通过海萨尼转换引入自然,将其转换为完全信息下的动态博弈得到贝叶斯扩展式,并证明了贝叶斯纳什均衡的存在性和唯一性。提出了一种基于高斯函数与扰动策略更新的粒子群算法对上述模型进行求解。仿真结果表明:改进算法相对其他算法有较快的收敛速度与较高的云制造系统总收益,不完全信息博弈模型能够兼顾不同类型的需求方提高云制造系统的总收益。

不可达系统的鲁棒贝叶斯估计方法

本文针对模型扰动下的不可达系统,提出了一种新的针对退化分布下的极大极小博弈问题的求解和证明方法.首先,文章将有相对熵约束的极大极小博弈问题转换成了一个无约束的拉格朗日函数,并找到其在均值和奇异的方差矩阵方向上都为严格凹函数的条件;其次,本文通过求解其均值和方差的极大值,得到所对应的鲁棒贝叶斯估计器和奇异的扰动状态误差协方差矩阵;最后,文章证明存在一个唯一的拉格朗日乘子满足其约束条件.微机电系统加速度计漂移估计仿真结果表明对所提算法的有效性.

计及马尔科夫链的能源社区产消者贝叶斯博弈策略

随着电力系统用户侧能源转型比例的不断提高,传统的社区用电居民逐步向着能源社区产消者转变,并通过能源交易的方式来促进分布式能源就地消纳。在此背景下,通过构建贝叶斯博弈能源交易模型,实现能源社区产消者之间的自主能源交易。首先,针对交易过程中产消者交易身份的不确定性问题,采用马尔科夫链对其身份状态概率进行分析。然后,将概率分布作为先验概率,建立能源社区产消者能源交易的贝叶斯博弈模型,对产消者之间的能源交易过程进行优化。最后,通过算例验证了所提模型在保证产消者隐私性的同时提高了其自身的收益。