MANUFACTURING SYSTEM SCHEDULING BASED ON MULTI-AGENT COOPERATION GAME

Aiming at the flexible manufacturing system with multi-machining and multi-assembly equipment, a new scheduling algorithm is proposed to decompose the assembly structure of the products, thus obtaining simple scheduling problems and forming the cOrrespOnding agents. Then, the importance and the restriction of each agent are cOnsidered, to obtain an order of simple scheduling problems based on the cooperation game theory. With this order, the scheduling of sub-questions is implemented in term of rules, and the almost optimal scheduling results for meeting the restriction can be obtained. Experimental results verify the effectiveness of the proposed scheduling algorithm.

Algorithm to Form Coalition in Multi-Agent Cooperation

In multi-agent systems, autonomous agents may form coalition to increase the efficiency of problem solving. But the current coalition algorithm is very complex, and cannot satisfy the condition of optimality and stableness simultaneously. To solve the problem, an algorithm that uses the mechanism of distribution according to work for coalition formation is presented, which can achieve global optimal and stable solution in subadditive task oriented domains. The validity of the algorithm is demonstrated by both experiments and theory.

Building Intelligent DSS Based on Multi-Agent Cooperation

A general multi-agent architecture is proposed for intelligent decision support system (MAIDSS). The agent in MAIDSS is built based on an extension of BDI framework. Several agents form a team working together on a decision problem; several agent teams are defined to stand for the benefits of different people in the real world. The decision making process is based on multi-agent cooperation, and a logical framework for a team of agents cooperating to create the solution for the decision problem is discussed in detail.

Trading platform for cooperation and sharing based on blockchain within multi-agent energy internet

With the release of the electricity sales side,large-scale small-capacity distributed power generation units are connected to the distribution side,forming multi-type market entities such as microgrids,integrated energy systems,and virtual power plants.With the large-scale integration of distributed energy,the energy market under the energy internet is different from a traditional transmission grid.It is currently developing in the direction of diversified entities and commodities,a flat structure,and a flexible and competitive multi-agent market mechanism.In this context,this study analyzes the value of combining blockchain and the electricity market presents the design of a blockchain trading framework for multi-agent cooperation and sharing of the energy internet.The nodes in market transactions are modeled through power system modeling in the physical layer and the transaction consensus strategy in the cyber layer;moreover,the nodes are verified in a modified IEEE 13 testing feeder of a distribution network.A transaction example is demonstrated using the multi-agent cooperation and sharing transaction platform based on the Ethereum private blockchain.

Roles of N-cadherin in cerebral cortical development:cooperation with membrane trafficking and actin cytoskeletal regulation

Cell adhesion plays pivotal roles in the morphogenesis of multicellular organisms.Epithelial cells form several types of cell-to-cell adhesion,including zonula occludens(tight junctions),zonula adhaerens(adherens junctions),and macula adhaerens(desmosomes).Although these adhesion complexes are basically observed only in epithelial cells,cadherins,which are the major cell adhesion molecules of adherens junctions,are expressed in both epithelial and non-epithelial tissues,including neural tissues(Kawauchi,2012).The cadherin superfamily consists of more than 100 members,but classic cadherins.

Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control

This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency.Firstly a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow densities.In order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is designed.The experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.

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.

Event-triggered robust cooperative output regulation for a class of linear multi-agent systems with an unknown exosystem

This paper investigates the robust cooperative output regulation problem for a class of heterogeneousuncertain linear multi-agent systems with an unknown exosystem via event-triggered control (ETC). By utilizingthe internal model approach and the adaptive control technique, a distributed adaptive internal model isconstructed for each agent. Then, based on this internal model, a fully distributed ETC strategy composed ofa distributed event-triggered adaptive output feedback control law and a distributed dynamic event-triggeringmechanism is proposed, in which each agent updates its control input at its own triggering time instants. It isshown that under the proposed ETC strategy, the robust cooperative output regulation problem can be solvedwithout requiring either the global information associated with the communication topology or the bounds ofthe uncertain or unknown parameters in each agent and the exosystem. A numerical example is provided toillustrate the effectiveness of the proposed control strategy.

Research on Cognitive Cooperation in Multi-Agent Systems

The accomplishment of a complex problem usually involves cooperation between participators with different knowledge background concerned. This paper identifies interdependency between different sub problems （through problem decomposition） as the major factor that influences cooperative relations in multi-Agent systems, based on which we propose an efficient means to measure cooperation coefficient （degree） between different Agents. Then cognitive cooperation between Agents is analyzed which aims at collecting the wisdom of the cognitive community for a systematic solution to the overall problem.

Rough set based multi-agent system cooperation for industrial supervisory interface system

In this paper, rough set theory is introduced into the interface multi-agent system （MAS） for industrial supervisory system. Taking advantages of rough set in data mining, a cooperation model for MAS is built. Rules for avoiding cooperation conflict are deduced. An optimization algorithm is used to enhance security and real time attributes of the system. An application based on the proposed algorithm and rules are given.

Multi-agent reinforcement learning with cooperation based on eligibility traces

The application of reinforcement learning is widely used by multi-agent systems in recent years. An agent uses a multi-agent system to cooperate with other agents to accomplish the given task, and one agent′s behavior usually affects the others′ behaviors. In traditional reinforcement learning, one agent takes the others location, so it is difficult to consider the others′ behavior, which decreases the learning efficiency. This paper proposes multi-agent reinforcement learning with cooperation based on eligibility traces, i.e. one agent estimates the other agent′s behavior with the other agent′s eligibility traces. The results of this simulation prove the validity of the proposed learning method.

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。

A study of cooperation processes in multi-agent systems of storm surge disaster

Collaborative governance mechanism is a public management process that emphasizes the establishment of trust relationship between various subjects within the government and between multiple subjects such as the government and non-government based on the needs of the interest community,so as to achieve the advantages of collaborative governance.It is an important measure to improve the national storm surge disaster management system and realize the modernization of disaster management capacity.It is also the trend of the government to improve public management.Based on the results of relevant national bulletins,the storm surge disaster is selected which is the most characteristic of Marine disasters in the scope of marine public management.We select Zhejiang Province as the research area,which is heavily affected by storm surge disaster.Based on the case subjects of previous major storm surge disasters in Zhejiang Province,we analyze the specific measures taken by relevant subjects to deal with storm surge disasters.This paper presents the current situation of the participants and the cooperation problems among the participants,finds out the causes of the problems,studies and puts forward countermeasures and suggestions for the coordination management among the participants,provides certain ideas for further developing the disaster prevention and reduction and emergency management of storm surge disasters in coastal areas in order to improve the understanding of multiple subjects on the emergency management of storm surge disasters.

STABILITY ANALYSIS AND COOPERATIVE CONTROL OF DISTRIBUTED MULTI-AGENT SYSTEM WITH SAMPLED COMMUNICATION

The cooperative control and stability analysis problems for the multi-agent system with sampled com- munication are investigated. Distributed state feedback controllers are adopted for the cooperation of networked agents. A theorem in the form of linear matrix inequalities（LMI） is derived to analyze the system stability. An- other theorem in the form of optimization problem subject to LMI constraints is proposed to design the controller, and then the algorithm is presented. The simulation results verify the validity and the effectiveness of the pro- posed approach.

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。

Multi-agent based modeling for order distribution cooperative negotiation in supply chains

With the new characteristics of global cooperation in supply chains being synthetically considered,a hybrid model to the cooperative negotiation process for the order distribution in supply chain is mainly studied.After reviewing and analyzing some main domestic and overseas processes in cooperative negotiation modeling in supply chain,some problems are subsequently pointed out.For example,the traditional simple multi-agent system(MAS)frameworks which have some limitations,are not suitable for solving modeling complex systems.To solve these problems,thinking with the aid of the multi-agent structure and complex system modeling,the manufacturing supply chain is taken as an example,and a time Petri net production model is adopted to decompose the materials.And then a cooperative negotiation model for the order distribution in supply chain is constructed based on combining multi-agent techniques with time Petri net modeling.The simulation results reveal that the above model helps solve the problems of cooperative negotiation in supply chains.

A Blackboard-Based Multi-Agent Cooperative Expert System Tool

Aim To design and implement a multi-agent cooperative problem solving expert system tool. Methods A blackboard system was adopted in the system as a data sharing and information exchanging center, to coordinate the complex cooperative problem solving. The system was developed in UNIX and MSWindows 95 mixed TCP/IP network environment. Results and Conclusion A prototype system of a multi-agent cooperative expert systems tool is implemented.The experiment demonstrates that the fundamental functions of a cooperative expert systems is realized.

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.

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.

Immune multi-agent model using vaccine for cooperative air-defense system of systems for surface warship formation based on danger theory

Aiming at the problem on cooperative air-defense of surface warship formation, this paper maps the cooperative airdefense system of systems （SoS） for surface warship formation （CASoSSWF） to the biological immune system （BIS） according to the similarity of the defense mechanism and characteristics between the CASoSSWF and the BIS, and then designs the models of components and the architecture for a monitoring agent, a regulating agent, a killer agent, a pre-warning agent and a communicating agent by making use of the theories and methods of the artificial immune system, the multi-agent system （MAS）, the vaccine and the danger theory （DT）. Moreover a new immune multi-agent model using vaccine based on DT （IMMUVBDT） for the cooperative air-defense SoS is advanced. The immune response and immune mechanism of the CASoSSWF are analyzed. The model has a capability of memory, evolution, commendable dynamic environment adaptability and self-learning, and embodies adequately the cooperative air-defense mechanism for the CASoSSWF. Therefore it shows a novel idea for the CASoSSWF which can provide conception models for a surface warship formation operation simulation system.