一个基于Multi-agent理论的个性化计算机导师系统

设计了一个基于Multi-agent的个性化计算机导师系统模型,引入了新颖的经验交流与共享学习机制,充分利用网上资源,通过协同式的强化学习,为用户提供个性化的学习资源,真正做到因材施教.

基于Multi-agent理论的电力系统计算机网络的安全防护体系设计

对基于Multiagent理论的电力系统计算机网络安全防护体系算法做了深入的研究,设计并实现了一种基于Multiagent理论的电力系统计算机网络的安全防护体系的分布式安全机制DSM的设计方案。该方案DSM结构分成低层和高层,低层提供加密的信道,采用混合使用RSA和Rabin加密的方法;高层实现权限的检查,采用访问许可表APL,由一个建立在电力系统计算机网络中的专门的认证中心ACAgent来完成各个Agent的登记、权限核实、修改草案等工作。通过理论分析和实践证明,该方案较好地满足了基于Multiagent的电力系统计算机网络中对安全性和实时性的要求。

基于Multi-Agent理论的复杂过程控制系统

Multi-Agent理论是分布式人工智能理论的一个重要组成部分,近年来得到迅速的发展。本文从工业过程控制生产发展的需要出发,提出了建立基于Multi-Agent理论的复杂过程控制系统的命题。文中简要定义了基于Multi-Agent理论的复杂过程控制系统,阐明了命题的必要性与可行性,并以较常见的复杂多输入多输出系统为例,尝试构建了基于Multi-Agent的自适应多输入多输出控制系统。

基于Multi-agent理论的智能计算机导师模型研究

基于Web的学习系统使学习者打破了时空的限制,加上社会对人才培养的个性化发展要求,因此基于Web的智能化教学系统是当前研究的热点.文中设计了一个基于Multi-agent的智能计算机导师的数学模型,引入了一种新颖的经验交流与共享学习机制,做到真正的人类教师无法完成的因材施教.

基于Multi-agent理论的社会网络文体分类方法

针对当前社会网络中的文体分类存在分类效果不理想问题,结合网络文体的多样性、多归属性及动态性的特征,提出了一种基于multi-agent的属性融合和词库关联的网络文体分类方法.首先提取网络文体的特征关键词和词义等基本属性,建立Multi-agent的融合分类模型,并给出了基于Multi-agent的社会网络文体融合分类算法.实验结果表明该方法与传统单分类器以及其他多分类器融合分类方法相比,不仅可以通过语义特征提取对语义网络中的网络文体进行高精度分类,而且可以实现社会网络文体分类的自动化,具有更高的分类精度与稳定性.

基于Multi-Agent理论的飞机故障协同诊断模型研究

随着我国民航事业的迅速发展,如何保障飞机的飞行安全成了日益重要的问题[1]。解决这一问题的关键就是及时准确地对故障进行分析和诊断。根据飞机远程故障诊断的实际需求及当前基于角色的协同诊断模型中存在的问题,应用Multi-Agent理论对民航飞机远程故障的协同诊断做了一些探索性研究。

实时分布式Multi-Agent控制理论的理想实现平台——现场总线控制系统

针对当前实时分布式 Multi- Agent控制理论缺乏赖以实现的通用系统平台现状 ,本文提出“将现场总线控制系统作为各种实时分布式 Multi- Agent控制理论实现的通用系统平台”这一新的命题 .文中通过分析现场总线控制系统的成因和体系结构特点 ,论证了该命题的正确性与可行性 ,并探讨了面对现场总线百家争鸣的现状应采用的选型策略 .该命题的提出 ,为实时分布式 Multi-

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。

基于Multi-Agent的水电站变压器故障诊断系统

为了精准、快速完成水电站变压器的故障诊断,设计基于Multi-Agent的水电站变压器故障诊断系统。变压器状态监控agent将检测到的变压器故障信息发送给系统管理agent,系统管理agent通过通信agent将变压器故障信息发送给变压器故障诊断agent,变压器故障诊断agent利用小波变换方法提取变压器故障特征,并将其作为IFOA-SVM模型输入,完成变压器故障分类后,获取变压器故障诊断结果,该结果通过通信agent显示给用户。实验表明,该系统可有效诊断变压器故障诊断,诊断成功率受系统故障信息丢失率的影响较小,诊断耗时、耗能小,并具有较高故障诊断成功率。

基于密度泛函理论纤维素热解机理热力学研究

为了探究纤维素的热解机理,参考相关的实验结果,以纤维素单体模化物为研究对象,采用密度泛函理论的方法,以b3lyp/6-31++g(d,p)为基,对纤维素单体热解反应生成乙醇醛和CO_(2)小分子的反应机理进行动力学研究.纤维素单体热解生成乙醇醛和CO_(2)的反应路径为纤维素单体首先开环,之后裂解生成四糖片段M2和乙醇醛P1,能垒最高为339.1 kJ/mol,四糖片段M2经过脱水形成烯酮结构后再与水分子作用生成羧基,最后发生脱羧反应生成CO_(2),能垒较高为290.1 kJ/mol,不容易发生,整个过程放出98.9 kJ/mol的热量,并对路径反应物、中间体和过渡态进行几何构型全优化,过渡态的振动和频率计算,同时进行不同温度下(400 K、600 K、800 K、1000 K、1200 K)热解过程的热力学分析,对纤维素的热解机理研究将对生物质的热裂解机理研究有重要的意义.

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

基于TRIZ理论的回水阀开口挡圈装配机创新设计

为解决地暖回水阀开口挡圈装配效率低的问题,基于TRIZ理论对回水阀开口挡圈装配机进行创新设计。通过九屏幕分析法提出了解决问题的方向,通过功能分析法得到了系统存在的问题,采用因果分析法得出了问题产生的根本原因。运用技术矛盾法、物理矛盾法、物-场模型法解决了装配开口挡圈效率低的问题,并提出了11条解决方案,进而设计出一种回水阀开口挡圈装配机。通过ANSYS软件进行压环装置的应力应变仿真,验证了装置强度的可靠性;通过ADAMS软件进行传动过程的运动仿真,验证了设计方案的合理性。本研究为回水阀开口挡圈装配机的创新设计提供了新思路。

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

加替沙星拉曼光谱、紫外吸收光谱及微观结构的理论研究

基于密度泛函理论(Density functional theory,DFT),M06-2X/6-311G(d,p)基组水平下对加替沙星分子的初始结构进行优化.计算其振动频率,采用VEDA4软件基于势能分布(Potential energy distribution,PED)计算结果对特征振动模式进行了归属指认,并和实验光谱进行了对比.绘制了分子表面静电势,分析分子可能发生亲电和亲核反应的位点.利用含时密度泛函理论(Time-dependent density functional theory,TDDFT)计算了加替沙星分子的激发态,讨论了加替沙星分子内的电子跃迁.该研究为分析加替沙星的光谱和电子结构提供了理论基础.

达卡巴嗪分子拉曼和紫外吸收光谱的理论研究

采用密度泛函理论(Density functionol theory,DFT),在B3LYP/6-31+g(d,p)基组水平上,对达卡巴嗪进行了结构优化,通过频率计算,获得达卡巴嗪的拉曼光谱,与实验获得的拉曼光谱进行对比,对400~2000 cm^(-1)频率范围内的拉曼光谱特征峰进行了指认归属.计算分析前线分子轨道和表面静电势,预测了达卡巴嗪分子化学反应发生的位点.采用含时密度泛函理论(Time-dependent density functional theory,TDDFT)计算了该分子的紫外吸收光谱和激发态,获得电荷转移光谱,分析了达卡巴嗪分子间的电荷转移关系.

使用合规农药能确保农产品“地头”质量安全吗?——基于豇豆农户的扎根理论分析

新的农产品质量安全法首次将小农户施药行为纳入监管范围,进一步厘清农户施药行为的影响机理与因素,有助于政府精准施策。通过对海南3个村32户农民豇豆种植过程的全程蹲点跟踪,对豇豆施药环节的参与式观察与深度访谈,运用扎根理论分析了访谈资料。研究发现:农户施药环节是一个复杂的行为选择过程,即使施用合规农药也不能确保农产品在产地的质量安全。在合规农药视角下影响农户施药行为选择的最重要因素是信息渠道,其次是农产品质量安全管理的政策强度、农产品市场价格和农村劳动用工成本。同时,邻里效应在农户施药行为选择中的作用越来越弱。在此基础上提出,要增加农户用药信息的有效供给渠道,加强农产品质量安全的全面检测,坚持为农户提供形式多样的有针对性的有效培训,增加重要食用农产品质量安全关键技术供给与推广,加强农产品“优质优价”市场建设的政策建议,并进一步指出海南自贸港建设和RECP协议实施背景下提升海南农产品质量安全水平的紧迫性。

Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning

Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable(MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience.Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.

A survey on multi-agent reinforcement learning and its application

Multi-agent reinforcement learning(MARL)has been a rapidly evolving field.This paper presents a comprehensive survey of MARL and its applications.We trace the historical evolution of MARL,highlight its progress,and discuss related survey works.Then,we review the existing works addressing inherent challenges and those focusing on diverse applications.Some representative stochastic games,MARL means,spatial forms of MARL,and task classification are revisited.We then conduct an in-depth exploration of a variety of challenges encountered in MARL applications.We also address critical operational aspects,such as hyperparameter tuning and computational complexity,which are pivotal in practical implementations of MARL.Afterward,we make a thorough overview of the applications of MARL to intelligent machines and devices,chemical engineering,biotechnology,healthcare,and societal issues,which highlights the extensive potential and relevance of MARL within both current and future technological contexts.Our survey also encompasses a detailed examination of benchmark environments used in MARL research,which are instrumental in evaluating MARL algorithms and demonstrate the adaptability of MARL to diverse application scenarios.In the end,we give our prospect for MARL and discuss their related techniques and potential future applications.

CoopAI-Route: DRL Empowered Multi-Agent Cooperative System for Efficient QoS-Aware Routing for Network Slicing in Multi-Domain SDN

The emergence of beyond 5G networks has the potential for seamless and intelligent connectivity on a global scale.Network slicing is crucial in delivering services for different,demanding vertical applications in this context.Next-generation applications have time-sensitive requirements and depend on the most efficient routing path to ensure packets reach their intended destinations.However,the existing IP(Internet Protocol)over a multi-domain network faces challenges in enforcing network slicing due to minimal collaboration and information sharing among network operators.Conventional inter-domain routing methods,like Border Gateway Protocol(BGP),cannot make routing decisions based on performance,which frequently results in traffic flowing across congested paths that are never optimal.To address these issues,we propose CoopAI-Route,a multi-agent cooperative deep reinforcement learning(DRL)system utilizing hierarchical software-defined networks(SDN).This framework enforces network slicing in multi-domain networks and cooperative communication with various administrators to find performance-based routes in intra-and inter-domain.CoopAI-Route employs the Distributed Global Topology(DGT)algorithm to define inter-domain Quality of Service(QoS)paths.CoopAI-Route uses a DRL agent with a message-passing multi-agent Twin-Delayed Deep Deterministic Policy Gradient method to ensure optimal end-to-end routes adapted to the specific requirements of network slicing applications.Our evaluation demonstrates CoopAI-Route’s commendable performance in scalability,link failure handling,and adaptability to evolving topologies compared to state-of-the-art methods.

Bipartite consensus problems of Lurie multi-agent systems over signed graphs: A contraction approach

This paper examines the bipartite consensus problems for the nonlinear multi-agent systems in Lurie dynamics form with cooperative and competitive communication between different agents. Based on the contraction theory, some new conditions for the nonlinear Lurie multi-agent systems reaching bipartite leaderless consensus and bipartite tracking consensus are presented. Compared with the traditional methods, this approach degrades the dimensions of the conditions, eliminates some restrictions of the system matrix, and extends the range of the nonlinear function. Finally, two numerical examples are provided to illustrate the efficiency of our results.