Abstract A Multi-Agent Based System to Enable Strategic and Operational Design Coordination

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Inter-agent communication in a FIPA compliant intelligent distributeddynamic-information systemLuís M. Botelho Rui J. Lopes Manuel M. Sequeira Paulo F. Almeida Sérgio MartinsADETTI/ISCTEAssociation for the Development of Telecommunications and Information Techniques1600 Lisbon PortugalABSTRACTThis paper describes a traffic surveillance system as a particularcase of the class of intelligent distributed dynamic-informationsystems (IDDIS). The Traffic Surveillance System is avision-based FIPA compliant multi-agent system that uses theFIPA Agent Communication Language (ACL) and the FIPASemantic Language (SL). The focus of the work is inter-agentcommunication and coordination. We have extended the SLexpressiveness with respect to the representation of uncertaintyand to the representation of ad hoc MPEG7 descriptions. Wepropose a transport encoding format more suitable fortime-constrained systems than the original textual formatproposed in the FIPA specifications. We show that, within thescope of the FIPA platform, the FIPA ACL is a communicationlanguage powerful enough to achieve multi-agent coordinationthrough communication. This work also suggests that the FIPAplatform is suitable for building surveillance basedapplications.Keywords: Intelligent Agents, Multi-agent communication andcoordination, Intelligent Distributed Dynamic-InformationSystems, IDDIS, Traffic Surveillance System, MODEST,FIPA, Agent communication language, ACL, Semanticlanguage, SL1.INTRODUCTIONThe MODEST project [9] is an ACTS [1] European projectwith two distinct purposes: a development purpose and aresearch purpose. The development purpose is to build avision-based Traffic Surveillance System based on a network ofvideo cameras placed along roads, tunnels, bridges, orhighways. The research purpose is to evaluate and contribute for the work of the FIPA [4] and the MPEG7 [10] standardization bodies. With this goal in mind, the MODESTproject conceived the Traffic Surveillance System as a FIPAcompliant intelligent multi-agent system. We have developed several aspects of the FIPA specifications [5][6] including inter-agent communication and some components of the FIPA platform.Inter-agent communication was addressed with MPEG7 inmind, that is, the agents in the system can exchange messagescontaining ad hoc MPEG7 descriptions. Another concern ofthe project is real-time. Design options were taken consideringthe demanding time-constraints imposed to real-time systems.This paper is essentially devoted to inter-agentcommunication and coordination although it also presents anoverview of the Traffic Surveillance System.Currently, the main goals of the Traffic SurveillanceSystem are to detect abnormal individual behaviors (such as "driving in zigzag"), to determine global traffic patterns (e.g., "traffic jams" and "very intense fast traffic") and to compute traffic macro indicators (e.g., statistics and pollution indexes).In this phase of the project, the Traffic Surveillance System is composed of four cameras placed along a bridge in Brussels. There is no overlap between the visual fields of the cameras. The cameras are fixed in specified locations and, apart from the tilt resulting from strong wind, they don’t move. The cameras don’t zoom nor pan, either. This restricted version of the Traffic Surveillance System can be viewed as a distributed information management system operating in a dynamic environment. Information-management system because it does not control any sensors nor effectors; distributed because each camera is connected to several computational agents acting autonomously in separate computers; dynamic because the vehicles enter the scope of the system in unpredicted instants of time with unpredicted positions and speeds.The Traffic Surveillance System described in this paper can be seen as an example of a class of information management systems hereafter termed Intelligent Distributed Dynamic Information Systems (IDDIS). Instead of operating over a relatively static database (as traditional Information Systems do) an IDDIS operates over dynamic physical processes. Instead of being composed of a single program that accesses and manages a single or multiple sources of information, an IDDIS is composed of several agents that access one or more information sources or distinct views of the same physical source. Besides information management, the main issues in such systems are inter-agent communication and multi-agent coordination. Figure 1 depicts the generic organization of an IDDIS.In general, we view Intelligent Distributed Dynamic Information Systems as a class of real-time systems because it is supposed that they interact with their environment, with their peers and with their clients, in real-time.Intelligent Distributed Dynamic Information Systems have two agent layers: the objective observation layer and the application layer.The agents in the objective observation layer observe the dynamic process(es) and cooperate with each other to build a distributed objective high-level description of the observed process. The agents in the application layer are mostly mutually independent although they may communicate. Each of them communicates with several observation agents in order to get the information required to build its own biased view of the available information. The role of yellow pages agents is obviously very important in an IDDIS. If an application agent wants to get some specific information, first it asks the yellow pages agent what is the name of the agent that provides such information.Examples of observed dynamic processes include highway traffic, Internet traffic, motion pictures, stock markets, plantproduction processes, organizational processes and multimedia animated environments. Examples of applications include traffic surveillance, investments advising, production scheduling and diagnostic systems.Since the MODEST is a very recent project, the preliminary test phase has just started. In this phase, the Traffic Surveillance System is tested off-line: it analyses images stored on tape captured from a single video camera.presents the architecture of the Traffic Surveillance System.Section 3 presents the knowledge representation scheme, the inter-agent communication mechanisms and messages used in the system, and the coordination mechanism adopted. The transport level of the agent inter-communication is described in section 4. Section 5 analysis the contributions of the work to the FIPA and MPEG7 standardization bodies. Finally, section 6 shows conclusions and points directions for future work.2.ARCHITECTURE OF THE TRAFFICSURVEILLANCE SYSTEMThe Traffic Surveillance System is an intelligent multi-agent system in a FIPA compliant platform. The whole system is composed by a collection of agents in the objective observation layer, by a collection of agents in the application layer and by the platform agents. The platform agents perform generic tasks for the other agents: agent management tasks and agent communication tasks.In the Traffic Surveillance System, the agents of the objective observation layer constitute the Camera Assistant subsystem; the agents of the application layer constitute the Application Assistant. Each camera has its own Camera Assistant. Some of the agents in the Application Assistant are associated to each camera whereas some others are not associated to the cameras. Besides these agents, there are other agents that belong to the FIPA Platform. These include the AMS ("Agent Management System"), the ACC ("Agent Communication Channel") and the DF ("Directory Facilitator",a yellow pages agent).Every agent that belongs to the Traffic Surveillance System must register (advertise) its services with the DF. Any agent can ask the DF to tell it the names of the agents that perform some required task.Camera AssistantEach Camera Assistant has a Camera Proxy Agent (CP) that represents the camera, as seen by all other agents. As far asagent and the camera is made via the Camera Proxy. The Camera Proxy uses a set of software tools to analyze the digitized images from the camera and to produce high level ad-hoc MPEG7 descriptions of the images. The CP delivers excerpts of those MPEG7 descriptions to all other agents in the Traffic Surveillance System that want to receive them.Besides the Camera Proxy, each Camera Assistant includes a Local-Site agent, a Classifier Agent, a Behavior Characterizer agent and a Tracker agent. All these agents work on objective,application independent representations of the external environment.The Local Site Agent maintains static representations of the road pertaining to the scope of the camera to which it is associated and the region between the camera and the next one.It also maintains dynamic representations of the typical trajectories of vehicles. The typical trajectories of the vehicles are determined by the Behavior agent.The static information about the road includes the characterization of each lane, the slope of the road, information regarding bends, information regarding legal and typical speeds, and also information used for the calibration of the camera.The Classifier agent classifies the observed mobile objects.For the time being, there are seven classes: car, van, truck,bike, motorcycle, person and very-long-vehicle. A very-long-vehicle is possibly not a vehicle, but the effect produced by several vehicles very close to each other moving with similar speeds.The Behavior agent computes the points of the typical trajectories of vehicles and describes the behavior of individualvehicles. Each point in a typical trajectory includes information regarding the speed and the position of the vehicle. A specific individual behavior description may be something like "very slow vehicle", "constant speed", "same lane". The Local Site agent stores information about typical trajectories computed by the Behavior agent.The Tracker is responsible for the identification of vehicles in two consecutive cameras. It receives descriptions of vehicles in its camera and compares them with descriptions of vehicles in the previous camera. When the descriptions are similar enough, the Tracker assumes they describe the same vehicle. The Tracker also detects new and missing vehicles. Application AssistantThe Application Assistant is composed by a set of agents, some of which are just user agents. The user agents are not associated to the cameras. Besides the user agents, the Application Assistant contains a Level of Service agent in each camera, an Abnormal Behavior agent in each camera, and some Pollution and Statistics agents.The Level of Service agent determines the global pattern of the traffic and its tendency. For instance, one may have an intense traffic with tendency to increase intensity (or to decrease it).The Abnormal Behavior agent associates degrees of alarm to the behaviors of individual vehicles (determined by the Behavior agent of the Camera Assistant), using knowledge of the application domain and of the local site. For instance, it may determine that a particular observed zigzag was not an instance of dangerous behavior, but was due to a momentary obstruction of one lane.The Statistics and Pollution agent computes several statistics and pollution indicators, such as the number of vehicles of each class that were observed in given location per hour.There may be several types of user agents. One such agent can decide to send a MPEG4 stream to the user, showing images of an accident. Another user agent may depict a graphical representation of the global level of service in the scope of the system. For this end, it uses the information about the level of service in each camera, creates a summarized version and produces a graphical representation.Thick arrows indicate ACL inter-agent communication. Dashed arrows indicate non-ACL communication. Small ovals represent individual agents. Large dotted ovals represent the Camera Assistant and the Application Assistant. The DF is represented as a square because it is a component of the FIPA Platform. The AMS and the ACC are not explicitly represented because there is no explicit interaction between them and the agents of the Traffic Surveillance System.Figure 2 - Traffic Surveillance System ArchitectureMUNICATION AND COORDINATIONAll inter-agent communication uses ACL, a speech act [14] based language. The contents of the ACL messages are expressed in extended SL (“Semantic Language”), a content language based in [13].This section covers two main aspects related to inter-agent communication and coordination: the expressiveness of the content language and the cooperation achievement capabilities of the agent communication language.First, the content language of the messages exchanged among agents enables the representation of ad hoc MPEG7 descriptions of complex objects such as snapshots (instantaneous view of the scene) and individual vehicles. The capability of representing such complex descriptions enables the agents to talk about arbitrary multimedia objects. We will see that the content language used enables the representation of two kinds of uncertainty: uncertainty of data and uncertainty of relations between objects. We show that the FIPA SL content language can easily be extended to exhibit the described properties.Second, we show that the FIPA ACL language supports the implementation of a flexible and efficient cooperation achievement mechanism that enables agents to coordinate their efforts to solve global goals and that allows the addition of new agents with new capabilities. This last feature supports the flexible and modular development of increasingly complex intelligent distributed dynamic-information systems. Extending SL: ad hoc MPEG7 descriptions and uncertainty The MODEST project adopted FIPA SL ("Semantic Language") as the content language of ACL messages. In this section, we extend the SL language to represent ad-hoc MPEG7 descriptions and uncertainty.In the Traffic Surveillance System, data entities such as object descriptions are sent as logical terms within the contents of ACL messages.In the lisp notation used by ACL and SL, the parenthesis around functional expressions come before the function symbol. For instance, (Car white 177 medium) would be used instead of the more usual Car(white, 177, medium). We use a special notation to represent possibly incomplete descriptions of compound objects. In this notation, a description starts with the constructor of the description type; the constructor is followed by a list of attribute-value pairs that represent the arguments of the constructor. These arguments are the components of the compound object. For instance the term (Car :position 177) is our notational convention for (Car unknown-color 177 unknown-size), in which Car is the constructor of the type car, and the constants unknown-size and unknown-color represent unspecified size and color, respectively.Complex descriptions can also be lists of terms. We use the function list with any number of arguments to represent lists.Any of the components of a compound object may be an uncertain term. In our extension of the SL language, we use the operator uncertain-object that takes a term and a confidence and returns an uncertain term, for instance (uncertain-object 177 0.9). The following grammar rules define the grammar of the extended SLTerms.ExtendedSLTerm =SLTerm |// original SL grammarDescription |Collection |UncertainTerm.Description =“(“ ConstructorSymbol ComponentSpec* “)”. ConstructorSymbol = FunctionSymbol. ComponentSpec = “:”RoleName Value.RoleName = Word.Value = ExtendedSLTerm.Collection =“(“ “list” ExtendedSLTerm+ “)”UncertainTerm=“(““uncertain-object”ExtendedSLTerm Confidence “)”.Confidence = RealNumber.In the proposed extension of the SL language, the special operator uncertain-proposition is used to represent uncertain propositions, for instance (uncertain-proposition (stopped obj125) 0.8). The following syntactic rules formalize this new kind of formula:ExtendedSLWff =SLWff |// original SL grammarUncertainProposition.UncertainProposition =“(“ “uncertain-proposition”SLWff Confidence “)”.Notice that the original uncertainty modal operator defined in the SL specification does not allow to say how much uncertain an agent is about a given proposition. In application domains in which the execution of certain actions depends on the confidence the agent has on its information, it is required that confidences be quantified.Coordination by Information-SubscriptionIn this section, we show that the FIPA ACL language is powerful enough to achieve coordination by communication.In a multi-agent system, coordination is achieved if agents cooperate with each other in a constructive way to achieve global goals or to solve individual problems. Coordination can be achieved in a variety of ways, ranging from the centralized control architectures [7] to the protocol-based approach [15] and to the emergent behavior approach [11].The coordination mechanism adopted in the Traffic Surveillance System represents a compromise between flexibility and efficiency. This mechanism is called information-subscription because it is useful for cases in which agents that need some information class from a provider agent must subscribe that information class with the provider. It is assumed that each agent in the Traffic Surveillance System registers (advertises) its services with the DF.Following a BDI-like understanding of agents rationality [2], if an agent wants another agent to perform some action on its behalf, it must send a message that creates the intention in the receiver of performing the action that is desired by the sender. This is the basic idea behind information-subscription. This coordination mechanism has already been suggested in [3], about the register and the service protocols.Although the intentional semantics of the FIPA ACL language has been subject to some criticism [12], it is suitable to implement the described coordination mechanism. Actually, the rational effect of the messages used by an agent to request some action from another agent is to create the intention on the receiver to perform the requested action. In particular, the query-ref performative is used to ask an agent what is the object that satisfies a given condition. This message has the desired result because, upon accepting it, the receiver becomes committed to send the requested information to the sender. The subscribe performative creates the persistent version of the intention that may be created using the query-ref performative.In the Traffic Surveillance System, the cameras and the associated image processing algorithms extract high-level descriptions from the images. The agents of the system receive all or part of the descriptions extracted from the image by the image processing algorithms. Each time a given description is available, each agent needs to receive the parts of it that are of interest to the agent. Therefore the coordination mechanism should provide an economic but flexible way to generate persistent intentions in the mental state of the providers to send the requested information to the consumers. Any agent in the system may play the role of a provider or a consumer or both.If an agent wants to receive some desired information, it must proceed as follows. First it asks the DF (“Directory Facilitator”) what is the agent that provides the required information. The DF replies with the name of the provider.Second, it sends one or more inform messages to the information provider defining the relation between the information produced by provider and the information it considers relevant. This relation is represented by a function from the descriptions of the provider agent to the descriptions of the requestor agent.Third, it sends a query-ref, a subscribe or a request-whenever message so that the provider creates the intention or the persistent intention to send the relevant information to the agent. This message requires the provider agent to apply the previously defined function to its descriptions and send the result to the requestor.The above three steps constitute the information-subscription coordination mechanism. This coordination mechanism works with agents that have the (implicit or explicit) socially oriented meta-intention of committing themselves to perform actions that are requested by some other agent once they have accepted the request.After an agent has subscribed some information class, it may send other messages canceling the subscription or updating the definition of the desired information.In the remaining of this section we present a sequence of FIPA ACL messages used in a particular instance of the described cooperation achievement process.Preliminary step. Registration with the DFIn the following message, an agent called Camera Proxy registers the capability of delivering mobile object descriptions with the DF.(request:sender (Agent Proxy 1):receiver (Agent DF 1):content(action(Agent DF 1)(register(:df-description(:agent-name (Agent Proxy 1))(:services(:service-description(:service-typeimage-description-delivery)(:service-ontologytraffic-surveillance-domain)))(:interaction-protocols (listfipa-request))))) :language SL0:ontology fipa-agent-management)The terms (Agent Proxy 1) and (Agent DF 1) represent the name of the Camera Proxy agent and the name of the DF agent of camera number 1.First step. Ask the DF to search the name of the provider. An agent called Classifier asks the DF to tell it the name of the agent that provides the image-description delivery service. (request:sender (Agent Classier 1):receiver (Agent DF 1):content(action(Agent DF 1)(search(:df-description(:service-typeimage-description-delivery)))) :reply-with (Message (Agent Classifier 1)16) :language SL0:ontology fipa-agent-management)In the message below, the DF informs the Classifier that, as a result of the requested search, it found that the Camera Proxy provides an image-description delivery service.(inform:sender (Agent DF 1):receiver (Agent Classifier 1):content(result(action(Agent DF 1)(search(:df-description(:service-typeimage-description-delivery)))) (:df-description(:agent-name (Agent Proxy 1))(:services(:service-description(:service-typeimage-description-delivery)(:service-ontologytraffic-surveillance-domain)))) :in-reply-to (Message (Agent Classifier1)16) :language SL0:ontology fipa-agent-management)The term (Message (Agent Classifier 1) 16) in the parameter :reply-with and :in-reply-to represents a unique message identifier composed by the agent identifier and by a sequential number. When the DF answers this request, it must specify the same message identifier.Second Step. Definition of the relevant data entities.In the following message, the Classifier defines the relationship between the descriptions managed by another agent (the Camera Proxy) and the descriptions that are relevant from the Classifier’s point of view. This relationship is represented by the function ClassifierObject/1. This function is applied to a mobile object description managed by the Camera Proxy and returns a mobile object description suitable for the Classifier.(inform:sender (Agent Classier 1):receiver (Agent Proxy 1):content(forall ?obj(=(ClassifierObject ?obj)(Cons (MObjectSize ?obj)(Cons (MObjectShape ?obj) null)))) :language ExtendedSL:ontology Traffic-surveillance-domain)MObjectSize is a function that takes a Camera Proxy mobile object and returns its size. MObjectShape is a function that takes a Camera Proxy and returns its shape.Third step. Creation of the desired (persistent) intention in the provider.In the following message, the Classifier tells the Camera Proxy: each time you have a new snapshot, pick each mobile object of that snapshot, apply the function ClassifierObject/1 and send me the result.(subscribe:sender (Agent Classier 1):receiver (Agent Proxy 1):content(iota ?x(exists ?snap(exists ?obj(and(last-snapshot ?snap)(member ?obj (objectsList ?snap))(= ?x (ClassifierObject obj)))))) :conversation-id (Message (Agent Classifier1) 34):language ExtendedSL:ontology Traffic-surveillance-domain)From this point on, the provider (i.e., the Camera Proxy) will send the relevant mobile object descriptions to the consumer agent (i.e., the Classifier).This coordination mechanism has the following advantages.1. The designer of an agent does not need to know whatother agents should receive the information produced by it. An agent just receives information-subscription messages. If it accepts the subscription, it must send the required information to the requestor.2. The designer of the agent does not need to know whatagents produce the required information. If an agent wants to know the name of the agent that produces the required information, it just asks the DF.3. The information-subscription can be made only once,usually during the initialization stage of the agent existence. This is much better than having to repeat the same query to the same agent demanding the same class of information. This is a specially important issue in time-constrained systems like the Traffic Surveillance System.All the above advantages mean we can create new application agents without having to modify the existing agents. It is worth noting that there isn’t any agent in the Traffic Surveillance System that plays the role of controlling the other agents.As a final remark, the previous description of the coordination mechanism assumes that all agents use the same vocabulary. However, if this is not the case, an agent can first ask the OA (“Ontology Agent”, another agent of the FIPA platform) to translate the necessary concepts. This would be the first step of the information-subscription mechanism. In the current implementation of the Traffic Surveillance System, the OA has not been implemented.4.TRANSPORT ENCODING FORMATIn the current stage of the project, it is assumed that there is a single agent platform (the MODEST platform), no inter platform interaction will occur, and no mobile agent will visit the MODEST platform. Thus, only a proprietary protocol is specified in the project for the efficient exchange of FIPA ACL messages. Two different types of requirements were defined for the protocol: transport mechanism requirements and message format requirements.Transport mechanism requirements:•reliable and ordered delivery of messages,•low overhead.One protocol that copes with these requirements is the TCP/IP protocol. Thus, TCP/IP Berkley sockets where used to implement the transport mechanism between agents in the MODEST platform.Message format requirements:•efficient coding of FIPA ACL messages,•fast interpretation of FIPA ACL messages.Figure 3 Message data and stream structureIn order to cope with these two requirements the FIPA ACL messages are stored in a message data structure as represented in Figure 3.In the message data structure, literal components are represented by numeric codes instead of the usual textualMessage Data StructureStream Structure。

Agent论文:AgentMulti-Agent-System动态集成框架模型脚本解释策略

Agent论文:AgentMulti-Agent-System动态集成框架模型脚本解释策略

Agent论文:Agent Multi-Agent-System 动态集成框架模型脚本解释策略【中文摘要】随着计算机软硬件技术的发展,软件系统的规模越来越庞大,功能也越来越复杂,如何有效的重用已有的软件单元,是目前很多研究的重点。

传统构件化的系统集成方法缺乏多角色,多用户,多层次的灵活的交互,使得集成后的系统缺乏必要的灵活性和柔性。

而人工智能领域的Agent技术,具有主动性,自治性,社会性和智能性等特性,将其应用到系统集成过程中就有可能解决面向多领域的、异构系统之间的、柔性的动态集成问题。

本文将Agent技术应用到软件系统集成领域,在对领域特征集成单元划分规则展开分析的基础上,提出了包装原集成单元的Agent模型,设计并实现了基于多Agent的系统动态集成框架模型。

把脚本语言中脚本的解释控制策略应用到系统集成过程中,提出用脚本定义集成规则、基于脚本解释控制来完成集成单元之间柔性的、动态的集成控制策略。

系统集成框架设计了Agent能力注册中心、Agent管理服务和公共消息黑板等三类管理Agent以及控制协调Agent并分发任务的控制Agent。

在单个Agent 独立求解的基础上,使用集中控制多Agent间交互的策略,柔性、动态的把被集成系统单元集成在一起。

最后将该框架模型应用到某领域仿真系统,在本文设...【英文摘要】With the development of computer software and hardware, software is becoming more hugeness and functional.It is point that how to use the existing software units in effect. The traditional method of component has the disadvantages of low flexibility and non-dynamic in the process of software system integration. With the properties of bounded autonomy, rationality, sociability, reactivity, cooperation, and responsibility, Agent is quite suitable for the software integration.This paper introduces agent t...【关键词】Agent Multi-Agent-System 动态集成框架模型脚本解释策略【采买全文】1.3.9.9.38.8.4.8 1.3.8.1.13.7.2.1同时提供论文写作定制和论文发表服务.保过包发.【说明】本文仅为中国学术文献总库合作提供,无涉版权。

基于多智能体SOA模型的电力系统信息集成的应用研究

基于多智能体SOA模型的电力系统信息集成的应用研究

第38卷第7期电力系统保护与控制Vol.38 No.7 2010年4月1日Power System Protection and Control Apr.1, 2010 基于多智能体SOA模型的电力系统信息集成的应用研究毕睿华,杨志超,王玉忠(南京工程学院电力工程学院,江苏 南京 211167)摘要:分析了电力系统信息的横向集成(集成化平台)和纵向集成(网络化平台)的特点和需求,提出在多智能体系统(MAS)模型上构建面向服务架构(SOA)结构体系解决电力信息系统集成问题;分析了电力信息系统的集成框架,提出了接口智能体(界面层)、信息集成总线和服务智能体(模型层)、决策/协调层的三层结构;分析了SOA的元模型机构,提出联邦管理智能体的概念,形成在SOA模型下的多智能体的联邦或联盟的协作关系。

关键词:SOA;多智能体系统;信息集成;服务智能体;联邦管理智能体Studies on information integration of MAS-based SOA model in power systemBI Rui-hua, YANG Zhi-chao, W ANG Yu-zhong(School of Power Engineering, Nanjing Institute of Technology, Nanjing 211167,China)Abstract: The characteristics and needs of horizontal integration(the integrated platform)and vertical integration(the network platform)in the power system information integration are analyzed and service-oriented architecture (SOA) built on the multi-agent system (MAS) model is introduced to solve the power system integration issues. The electric power information system integration framework is analyzed and three-tier structure including the interface agent (interface layer), UIB and SA, organization layer is introduced. The SOA meta-model structure is analyzed and the federation management agent is introduced to construct the collaborative relationships among the multi-agents built on SOA.Key words: SOA; multi-agent system; information integration; SA; federation management agent中图分类号:TM71 文献标识码:A 文章编号: 1674-3415(2010)07-0063-060 引言在“以信息化带动工业化,以工业化促进信息化”的战略构想的指引下,电力系统信息化建设服务于电力系统企业的市场化运营,为企业的未来积聚竞争优势。

多智能体系统在微电网中的应用

多智能体系统在微电网中的应用

第45卷第2期2021年4月南京理工大学学报JournalofNanjingUniversityofScienceandTechnologyVol.45No.2Apr.2021㊀收稿日期:2020-07-07㊀㊀修回日期:2020-09-24㊀基金项目:江苏省自然科学基金(BK20161499)㊀作者简介:张善路(1990-)ꎬ男ꎬ博士生ꎬ主要研究方向:电力系统ꎬ电力电子功率变换器ꎬE ̄mail:zhangshanlu312@126.comꎻ通讯作者:李磊(1975-)ꎬ男ꎬ教授ꎬ博士生导师ꎬ主要研究方向:电力系统分析㊁电力电子应用㊁先进储能及电源技术智能电网ꎬE ̄mail:lileinjust@njust.edu.cnꎮ㊀引文格式:张善路ꎬ李磊ꎬ陈鹏威ꎬ等.多智能体系统在微电网中的应用[J].南京理工大学学报ꎬ2021ꎬ45(2):127-141.㊀投稿网址:http://zrxuebao.njust.edu.cn多智能体系统在微电网中的应用张善路ꎬ李㊀磊ꎬ陈鹏威ꎬ刘佳乐(南京理工大学自动化学院ꎬ江苏南京210094)摘㊀要:分布式电源的复杂和多样性增加了微电网能量管理和控制的难度ꎬ因此基于多智能体系统(Multi ̄agentsystemꎬMAS)的分布式分层协同控制策略被提出ꎬ其具有平衡功率和能量㊁稳定电压和频率㊁实现资源优化管理和经济协调运行的优点ꎮ该文主要对MAS在微电网中的应用情况进行全面系统的分析㊁对比㊁归纳总结ꎮ对比分析了微电网分层控制策略ꎬ研究表明基于MAS的分布式分层控制可以提高系统灵活性㊁可靠性ꎮ研究了不同的MAS建模方法的优缺点ꎬ为优化控制策略的选择提供依据ꎮ对通信时延㊁一致性协议㊁即插即用拓扑等方面进行阐述ꎬ综合分析了不同通信补偿方法ꎮ归纳出下一步基于MAS的分布式分层协同控制与优化的研究方向ꎮ关键词:智能体系统ꎻ微电网ꎻ分层协同控制ꎻ通信延迟ꎻ一致性中图分类号:TM732㊀㊀文章编号:1005-9830(2021)02-0127-15DOI:10.14177/j.cnki.32-1397n.2021.45.02.001Applicationofmulti ̄agentsysteminmicrogridZhangShanluꎬLiLeiꎬChenPengweiꎬLiuJiale(SchoolofAutomationꎬNanjingUniversityofScienceandTechnologyꎬNanjing210094ꎬChina)Abstract:Thecomplexityandvarietyofdistributedgenerationincreasethedifficultyofenergymanagementandcontrolofmicrogridꎬanddistributedhierarchicalcoordinatedcontrolstrategiesareproposedbasedonthemulti ̄agentsystem(MAS)ꎬwhichshowstheadvantagesofbalancingthepowerandenergyꎬstabilizingvoltageandfrequencyꎬandachievingeconomicandcoordinatedoperationinmicrogrid.ThispapermakesacomprehensiveandsystematicanalysisꎬcomparisonandsummaryoftheapplicationoftheMASinmicrogrid.Firstlyꎬthehierarchicalcontrolstrategiesofmicrogridarecomparedandanalyzed.Theresearchshowsthatdistributedhierarchicalcontrolbased南京理工大学学报第45卷第2期onMAScanimprovetheflexibilityandreliabilityofthesystem.SecondlyꎬthemeritsanddrawbacksofdifferentMASmodelingmethodsarestudiedtoprovideabasisfortheselectionofoptimalcontrolstrategy.Withrespecttothecommunicationdelayꎬconsensusprotocolꎬplugandplaytopologiesareelaboratedꎬandthedifferentcommunicationdelaycompensationsstrategiesmethodsarecomprehen ̄sivelyanalyzed.FinallyꎬthefuturetrendsintermsofdistributedhierarchicalcoordinationcontrolstrategiesandoptimizationschemesbasedontheMASaresummarizedandproposed.Keywords:multi ̄agentsystemꎻmicrogridꎻhierarchicalcoordinationcontrolꎻcommunicationdelayꎻconsensus㊀㊀随着对可再生能源需求的增加ꎬ以清洁能源为主的光伏㊁风力发电等可再生能源的分布式电源已经在微电网中广泛应用ꎮ这种分布式发电比集中式发电具有更大的灵活性ꎬ在未来的智能电网中必将代替传统的发电模式ꎮ为了实现大电网和分布式电源之间功率平衡和能量管理问题ꎬ充分发挥分布式电源灵活㊁高效㊁易扩展的优点ꎬ微电网的概念被提出[1]ꎮ基本的微电网结构如图1所示ꎬ由分布式电源㊁传统发电机㊁能量转换装置㊁能量存储系统㊁负荷等组成ꎮ主要通过微电网集中控制中心或者能量管理系统进行控制ꎮ它比单个分布式电源单元具有更高的灵活性ꎬ能够实现自我控制㊁保护和管理ꎮ微电网的应用已经从根本上改变了传统负荷供电的方式ꎬ实现了分布式电源即插即用的目的ꎬ提高了电能质量ꎮ同时ꎬ有效地解决偏远地区供电问题以及避免由于大面积停电事故所造成的损失ꎬ极大地改善了电网的安全性㊁灵活性和可靠性[2]ꎮ通常微电网有3种工作模式:并网模式㊁孤岛模式以及两种模式之间的切换模式ꎮ微电网是通过公共连接点(PointcommonconnectꎬPCC)与大电网连接实现功率双向流动和模式转换的ꎮ在并网模式下ꎬ微电网不仅可以通过能量装换装置把电能回馈到大电网ꎬ同时当微电网自身发电不足时大电网也可以将电能传输到微电网ꎮ在孤岛模式下ꎬ微电网作为独立供电电源能够平衡本地负载的有功和无功功率ꎬ以确保系统的稳定运行ꎮ图1㊀微电网结构示意图821总第237期张善路㊀李㊀磊㊀陈鹏威㊀刘佳乐㊀多智能体系统在微电网中的应用㊀㊀㊀㊀微电网的发展已经越来越成熟ꎬ但是目前仍然面临一些挑战ꎬ比如缺乏大规模可再生能源的并网能力ꎬ特别是在配电网条件较弱的情况下ꎬ并网能力更差ꎮ同时ꎬ电动汽车和储能技术的发展对智能微电网技术也提出了迫切的需求ꎮ而且要求多个微电网可以并联组成微电网群㊁提高系统稳定性以及电能质量㊁加强能量管理机制ꎬ优化和改进控制性能等问题已经受到越来越多的关注[3ꎬ4]ꎮ此外ꎬ微电网群也越来越受到研究者的关注ꎬ它是由多个基本微电网单元组成ꎮ微电网群出现的目的是在传统的分布式网络基础上增加微电网的渗透率ꎬ实现可再生能源的高效和稳定运行以及与大电网的友好交互[5ꎬ6]ꎮ作为一种高效处理可再生能源间歇性和随机性的方法ꎬ微电网群已经在多篇文献中被讨论ꎮ此外ꎬ微电网群还可用于处理分布式协调问题ꎬ同时保证系统的稳定运行ꎮ目前对于微电网的协同控制策略主要有3种类型:集中式控制㊁分布式控制以及分层控制ꎮ在集中式控制策略中ꎬ会设置一个主控制器ꎬ其能够对整个电网的数据信息进行处理ꎬ并将最终的决策指令发送到执行单元ꎬ从而实现预设的控制目标[7]ꎮ同时在主-从控制器之间需要设置一种通信转换语言来实现上述的信息传输ꎮ这种控制在技术难度和风险方面相对较低ꎬ但是一旦主控制器或者通信发生故障ꎬ整个微电网将不能正常工作ꎬ系统的可靠性将会受到严重损坏ꎮ为了避免上述问题的出现ꎬ提出了分布式控制ꎬ它是每个模块都有自己独立的控制器ꎬ其根据本地信息就能实现自我管理和控制[8]ꎬ避免了由于通信线路故障引起的可靠性问题ꎬ具有很好的扩展性ꎮ但是模块之间工作的独立性使得信息交流缺乏ꎬ难以实现系统整体控制和优化ꎮ结合前两者的优点ꎬ提出了分层控制ꎬ它将多智能体技术应用到微电网控制中ꎮ其利用多智能体的自治性㊁交互性㊁协调性的特点既能实现本地单元的独立运行ꎬ又能实现上层的优化控制和能量管理以及经济调度等[9ꎬ10]ꎮ分布式多智能体控制方法已被广泛应用于通过建立系统模型来加强电网可靠性和能量管理以及优化和改进系统性能等方面ꎮ本文对多智能体系统(Multi ̄agentsystemꎬMAS)模型进行了综述ꎬ包括图拓扑模型㊁遗传算法㊁非合作博弈模型和粒子群优化算法等ꎮ此外ꎬ在复杂的系统中一致性协议是多智能体之间相互交互的最基本的运行机制ꎬ它描述的是智能体之间信息交互的过程以及收敛最优ꎮ在多智能体系统中一致性协议是实现整个协调控制最重要的方向之一ꎮ在本文中ꎬ对基于多智能体的一致性协同控制方法进行了系统的综述ꎮ同时ꎬMAS的运行依赖于通信链路ꎬ不可避免会引起通信延迟稳定性问题ꎮ通信延迟主要分为固定通信延迟和随机通信延迟ꎬ本文分别对其各种补偿方案进行了比较ꎮ对基于MAS的微电网的研究ꎬ国外已经取得了很大的进展ꎮ国内在该领域的研究尚不成熟ꎬ缺少该领域的综述性文章ꎮ本文将结合国内外研究现状ꎬ对微电网基于MAS的分布式协调控制和优化进行了详细阐述分析ꎬ如建模方法㊁一致性控制㊁通信延迟㊁即插即用切换拓扑㊁能量协调㊁经济调度等问题ꎮ最后ꎬ给出了下一步研究方向ꎬ为该领域的研究学者提供参考ꎮ1㊀微电网中的分层控制微电网拓扑结构多变㊁控制结构复杂㊁控制目标多样ꎬ因此专家学者提出了微电网分层控制理论ꎬ它是以实现每一层的分布式控制为目的ꎬ最终实现微电网有功和无功功率㊁频率㊁电压的控制ꎬ以及各个分布式电源之间的能量协调㊁经济调度等ꎮ同时ꎬ无论是在并网模式还是孤岛模式下微电网的运行必须满足功率平衡的要求来保证系统电压和频率的稳定ꎮ微电网是一个复杂的多目标控制系统ꎬ它显示了多重时间尺度属性ꎬ如何在不同时间尺度下处理负载功率分配问题以及调节电压㊁频率和电能质量的稳定性是首先需要解决的关键问题[11-15]ꎮ为了恰当地应对这些问题ꎬ分层控制作为一种常见㊁有效的用于解决分布式电源的并网方法已得到广泛认可ꎮ1.1㊀传统的分层控制策略传统的分层控制主要是集中式控制ꎬ控制方式不够灵活ꎬ存在单点故障点ꎬ过度依赖通信网络ꎮ整体控制框图如图2所示ꎬ主要包括:初级控制㊁二级控制和三级控制ꎮ对于初级控制采用的是下垂控制ꎬ为了调节功率㊁电压㊁电流ꎬ避免电压和频率的不稳定以及解决多个微电网能量分配问题[16-18]ꎮ下垂控制方程如下㊀ω=ω∗-m (P-P∗)(1)㊀E=E∗-n (Q-Q∗)(2)921南京理工大学学报第45卷第2期式中:ω㊁E分别为输出电压参考值的频率和幅值ꎬω∗㊁E∗为额定参考角频率和电压ꎮP㊁Q是有功功率和无功功率ꎬP∗㊁Q∗是额定有功功率和无功功率参考值ꎮm㊁n为下垂控制系数ꎮ初级控制主要用于平衡分布式电源和储能装置之间的能量ꎮ图2㊀微电网分层控制结构示意图㊀㊀二级控制主要为消差环节ꎬ目的在于消除由初级控制层产生的频率和电压的偏差ꎬ将频率和电压维持在额定值附近[19-21]㊀Δω=1nðni=1Δωi=1nðni=1mi(Pi-P∗i)(3)Δω为角频率补偿量平均值ꎻΔωi为各台逆变器的角频率补偿量ꎮ进一步化简得到㊀Δω=mip∗i(1nðni=1Pi(pꎬu)-1)=㊀㊀K1(1nðni=1Pi(pꎬu)-1)(4)式中:Pi(pꎬu)=Pi/P∗iꎬ为各台逆变器的实际有功功率的标幺值ꎮ在微电网的二级控制中ꎬ集中控制和分散控制是最常用的方法[22-24]ꎮ对于集中式控制来说ꎬ最大的问题是过度的依赖微电网中心控制器ꎬ当微电网中心控制器处于故障状态时就会导致整个系统瘫痪ꎮ而且在这种集中式控制架构下是需要双向通信网络拓扑ꎬ增加了通信频道中数据信号处理的难度ꎮ同时由于通信延迟问题ꎬ测量和控制信号在传输过程中不可避免的存在延迟或者丢失的现象ꎮ在这种情况下ꎬ一方面会增加微电网的网络维护成本ꎬ另一方面也大大降低系统的稳定性[25-29]ꎮ为了解决上述问题ꎬ提出了分散式控制策略ꎮ分散式控制不依赖于微电网中心控制器和下垂控制机制ꎬ因此当某个分布式电源发生故障不会造成整个系统崩溃ꎮ同时ꎬ该控制策略还具有更好的通信容错的能力ꎬ也可以实现即插即用的性能ꎬ并且很容易扩展到更多的分布式电源单元ꎬ使得系统具有更好的可扩展性[30ꎬ31]ꎮ三级控制为调度层ꎬ控制各个分布式电源之间及微电网与外界的功率流动[32]ꎮ三级控制是微电网控制中最高水平控制ꎬ它可以根据系统状态㊁市场情况和需求预测来进行决策ꎬ优化微电网的容错能力和运行状态[33]ꎮ当微电网运行在并网模式下ꎬ通过调节电压频率和幅值可以控制能量在微电网内部的流向ꎮ㊀ω∗MG=kp(P∗G-PG)+kiʏ(P∗G-PG)dt(5)㊀E∗MG=kp(Q∗G-QG)+kiʏ(Q∗G-QG)dt(6)式中:kp㊁ki是三级控制补偿器的控制参数ꎬ根据P∗G和Q∗G额定有功功率和无功功率参考值ꎬ可以计算出实际的微电网出力情况[34]ꎮ1.2㊀基于MAS的分布式分层控制策略在传统的微电网分层控制中不能实现对电压㊁频率㊁功率的高智能性㊁强扩展性㊁高冗余和高可靠性的调节ꎮ作为一种智能控制方法ꎬ多智能体控制策略被逐渐应用到微电网中ꎮ多智能体控制的主要思想就是将复杂的大规模的系统分成若干个子系统ꎬ并且每个子系统之间都具有自治性和交互性的特点ꎮ文献[35]中ꎬ给出了Agent的031总第237期张善路㊀李㊀磊㊀陈鹏威㊀刘佳乐㊀多智能体系统在微电网中的应用㊀㊀定义ꎬ认为一个Agent是具备自治性㊁社会性㊁反应性和主动性的建立在计算机平台之上的软硬件系统ꎬ即一般智能体具有以下3个特征[36-38]ꎮ(1)反应性ꎮ每个智能体都能够对其环境中的变化及时的做出反应ꎬ并根据这些变化和它要实现的功能采取一些应对措施ꎮ(2)主动性ꎮ每个智能体不仅仅能感知和响应环境变化ꎬ而且还表现出目标导向的行为ꎮ目标导向行为是指为了实现目标ꎬ智能体会动态地改变自己的行为ꎮ例如ꎬ如果一个代理丢失了与另一个代理的通信ꎬ而它需要另一个代理的服务来实现其目标ꎬ那么它将搜索提供相同服务的另一个代理ꎮWooldridge教授把它定义为一种主动能力ꎮ(3)社会性ꎮ每个智能体都能够与其他智能体进行信息交互ꎮ社交能力不仅仅意味着在不同的软件和硬件实体之间简单地传递数据ꎬ它还具有以合作的方式谈判和互动的能力ꎮ这种能力通常由智能体通信语言(AgentcommunicationlanguageꎬACL)支持ꎬACL允许智能体进行交谈ꎬ并完成协调㊁协作和协商等交互ꎮ通过每个子系统的智能特性利用多智能控制策略能实现系统的合作运行ꎬ因此适用于微电网中分布式电源的控制[39]ꎮ在近几年的文献中ꎬMAS已经广泛地应用在微电网中ꎮ其中ꎬ文献[40]提出将MAS应用到孤岛微电网的能量管理中并取得良好效果ꎮ文献[41]提出的多智能体策略实现了微电网中混杂的储能装置间的能量分配问题ꎮ文献[42]提出MAS模式下的分散控制在不同的通信网络下通过建立不同控制规则实现控制目标ꎮ当外界环境和负荷都在变化的情况下ꎬ依然能够输出稳定的电压㊁频率和功率ꎮ文献[43]提出基于分布式多智能体的频率控制方法ꎬ每个智能体能够跟相邻的智能体进行通信ꎬ通过采用平均一致性控制策略ꎬ使得控制目标达到最优ꎬ而且所有的信息都能通过这种分布式控制方法被共享ꎮ同时ꎬ在文献[44]中建立了基于MAS的分散式协同控制策略ꎮ文献[45]中提出一种基于MAS的分布式自适应控制设计方法ꎬ能够解决下垂控制中存在的问题ꎬ消除电压和频率偏差ꎬ实现有功和无功功率的合理分配ꎮ随着多智能体理论的发展ꎬ将分布式电源看作智能体并将其应用于微电网控制和管理ꎬ能实现分布式电源的 即插即用 性能ꎬ使得控制更加灵活ꎮ但是ꎬ分布式电源单元之间复杂多样的组合方式给实时控制的实施带来了很大的困难ꎬ也显著增加了系统运行的复杂性ꎮ为了实现MAS的最优运行ꎬ需要建立一个合适的综合优化运行模型ꎬ该模型必须与微电网的架构和运行模式密切相关ꎬ以实现微电网分布式协调控制[46-48]ꎮ2㊀微电网中MAS的建模与一致性由于MAS中分布式控制系统的复杂性使得系统难以控制ꎮ为了设计最优配置和最优控制策略ꎬ需要建立相应的系统模型ꎬ包括微电网拓扑模型和数学模型ꎮ同时ꎬ在复杂的动态模型中一致性是一个很重要的问题ꎬ其表明随着时间的变化ꎬ所有的智能体的状态最终都能收敛到最优值[49ꎬ50]ꎮ2.1㊀基于MAS的分布式分层控制策略在基于MAS的拓扑建模中ꎬ图模型是一种被广泛接受的方法ꎮ在文献[51]中ꎬ提出一种将任意可能非整数平均k次的连通图转化为连通随机m-正则图的离散方案ꎮ通过所提出的局部操作优化图的连通性ꎬ在总体稀疏性变化最小的情况下提高了网络的鲁棒性ꎮ在文献[52]和[53]中提出一种基于图论的多智能体系统的分布式非周期模型预测控制方法ꎬ该模型可以对图中的节点数量约简ꎬ并生成一个降阶的加权对称有向图MAS模型ꎮ在文献[54]中ꎬ研究了一般线性多智能体系统的符号一致问题ꎬ针对几种图拓扑结构ꎬ提出了分布式控制律ꎮ在文献[55]中ꎬ设计了连接实际通信链路的分布式地面站的加权图模型ꎬ如图3所示ꎮAi表示第i个分布式电源DGiꎬ每个Ai可以看作是一个Agentꎬ节点之间的连线表示两个分布式电源之间存在交互作用ꎮ该设计不需要微电网拓扑㊁阻抗或负载的信息ꎬ结构简单ꎬ冗余度高ꎬ易于扩展ꎬ消除了对中央微电网控制器的依赖ꎮ因此ꎬ为了实现MAS的全局优化ꎬ需要在系统状态和远程控制输入之间进行大量的数据通信ꎬ这导致了底层通信网络的高成本[56]ꎮ为了实现经济上可行通信ꎬ在通信成本或稀疏性约束下ꎬ根据通信状态/控制输入对的数量ꎬ文献[57]提出了一个博弈论框架ꎮ随着这种约束的加强ꎬ系统将从密集通信过渡到稀疏通信ꎬ从而在动态系统性能和信息交换之间实现权衡ꎮ131南京理工大学学报第45卷第2期图3㊀多智能体的图模型结构除了上述方法外ꎬ还提出了遗传算法㊁粒子群优化算法(ParticleswarmoptimizationꎬPSO)等数学模型来应用于多目标控制系统ꎮ在文献[58]中ꎬ提出MAS与遗传算法相结合ꎬ形成一种求解全局数值优化问题的多智能体遗传算法ꎬ该算法具有可扩展性ꎬ还可以提高MAS的预测精度和收敛速度ꎮ针对网络可靠性问题ꎬ文献[59]提出一种基于蒙特卡罗仿真(MonteCarlosimulationꎬMCS)的粒子群优化算法ꎬ所提出的MCS ̄PSO可以在可靠性约束下使成本最小化ꎮ这也是首次尝试使用粒子群算法结合MCS来解决复杂的网络可靠性问题ꎬ而不需要事先了解可靠性函数ꎮ与以往的研究工作相比ꎬMCS ̄PSO算法能够更好地解决复杂网络的可靠性优化问题ꎬ具有更高的效率ꎮ在文献[60]和[61]中ꎬ提出了一种改进二进制的粒子群优化算法ꎮ利用实时数字模拟器对电力系统进行建模ꎬ利用JAVA开发出一种基于PSO的多代理负载频率控制(Loadfrequencycon ̄trolꎬLFC)算法与资源代理通信ꎬ提高了孤岛运行下频率和电压的稳定ꎮ因此ꎬ适当地建立管理系统模型是协调控制和分析系统稳定性的前提ꎮ利用这些方法ꎬ可以实现微电网间的友好交互ꎬ实现新能源的有效利用[62]ꎮ表1对前面所述的建模方法和优化算法的优缺点进行了总结ꎮ表1㊀基于MAS的建模方法在微电网中优缺点比较模型和算法优点缺点图论拓扑模型[51-55]模型结构简单冗余度高㊁易于扩展对鲁棒性影响很大博弈模型[57]每个智能体都能实现状态优化算法复杂且耗时遗传算法[58]预测精度高ꎬ收敛速度快可扩展性和并行运行大多数参数根据经验获得动态响应速度慢粒子群优化算法[59]模型结构简单ꎬ计算速度快经济调度高效不能处理离散优化问题改进二进制粒子群优化算法[60ꎬ61]全局搜索性能好能处理离散优化问题缺乏后期的局部搜索能力2.2㊀分布式MAS的一致性在多智能体系统中ꎬ信息交互是指单个智能体与其相邻智能体之间的相互通信作用ꎮ因此ꎬ在智能体系统中实现控制目标一致性是关键问题[63]ꎬ包括对网络变换拓扑的一致性㊁对延迟的一致性㊁对最优目标的一致性㊁对采样数据的一致性ꎬ自适应一致性ꎬ二阶一致性ꎬ多个智能体的一致性[64-69]ꎮ文献[70]提出了一种分布式k均值算法和一种分布式模糊c均值算法ꎮ利用多智能体一致性理论中的一致性算法来交换传感器的测量信息ꎮ通常ꎬ这些问题是由分布式协议处理的ꎬ其中文献[71-73]设计了一个状态观测器和一个干扰观测器ꎬ保证一致误差为零ꎬ完全抑制干扰ꎮ此外ꎬ状态观测器采用自适应耦合增益的全分布方式设计ꎬ其优点是一致性协议的设计不依赖于与通信网络相关联的拉普拉斯矩阵ꎮ文献[74]提出一种通信时延下的线性协商协议ꎬ解决了MAS中的参数不确定性和时延问题ꎮ在这种方法中使用的协商一致协议表达式如下㊀ui(k)=KðjɪNiaij(xj(k-(k))-xi(k-(k))(7)式中:ui(k)和xi(k)分别为协商一致协议和第i231总第237期张善路㊀李㊀磊㊀陈鹏威㊀刘佳乐㊀多智能体系统在微电网中的应用㊀㊀个智能体的状态ꎮK是具有合适维数的反馈增益矩阵常数ꎬ(k)代表了时变延迟ꎮ让δij(k)=xj(k)-xi(k)表示状态之间智能体j和i的误差ꎮ定义离散时间MAS的成本函数JC如下㊀JC=JCx+JCu(8)㊀JCx=ðɕk=0ðNi=1ðNj=1aijδTij(k)Qxδij(k)(9)㊀JCu=ðɕk=0ðNi=1uTi(k)Quui(k)(10)式中:JCx和JCu分别为离散时间MAS的一致调节性能和控制能耗ꎮQx和Qu是对称的正定矩阵ꎮ对于给定的反馈增益矩阵Kꎬ在任意给定的有界初始条件下ꎬ离散时间MAS都能达到鲁棒性的成本一致ꎮ文献[75-77]提出两种情况下的高阶的一致协议:(1)状态反馈控制ꎬ它假设每个代理都可以访问其自身的状态以及其相邻的相对位置ꎻ(2)输出反馈控制ꎬ其中每个代理只测量其自身的位置及其相邻的相对位置ꎮ通过两个实例分析ꎬ说明了所提方案的优越性和有效性ꎮ在文献[78]和[79]中ꎬ建立了一种基于MAS的分布式混合控制策略ꎬ以确保微电网运行模式转换过程中的稳定性ꎻ设计了一种基于分布式稀疏通信网络的二级优化控制器ꎬ可以实现微网内负荷波动时元件上电压㊁频率的快速恢复以及有功功率的精确分配ꎮ文献[80-82]提出一种基于状态观测器的分布式输出反馈控制方案ꎬ保证了MAS的一致性ꎮ此外ꎬ还设计了状态反馈控制来处理MAS中的一致性问题ꎮ文献[83]提出一种克服延迟和噪声干扰的新技术ꎬ采用了增益衰减满足持久性条件的一致性协议ꎮ在微电网系统中ꎬ基于分布式MAS的动态一致性协议得到了广泛的认可ꎮ可以保证微电网的电压和频率稳定ꎬ有效调节有功功率和无功功率ꎮ同时ꎬ在线路阻抗不平衡㊁负载不平衡和非线性等复杂情况下ꎬ也可以改善微电网的电能质量[84ꎬ85]ꎮ3㊀微电网中MAS的通信时延分析智能微电网的发展离不开通信网络的支持ꎮ而通信时延是微电网控制实际应用中的主要障碍ꎮ尤其基于多智能体系统的微电网涉及的通信要求精度更高㊁控制更复杂ꎮ因此ꎬ如何改善和优化通信时延问题ꎬ对于单个微电网系统及微电网群的协调控制稳定运行至关重要ꎮ虽然华为5G通信技术已经成熟并领先世界ꎬ但是在整个国家电力系统中还没有普及ꎮ因此ꎬ研究通信机制㊁优化通信时延补偿是目前和未来一个重要的研究方向[86-88]ꎮ3.1㊀MAS的通信机制通信时延是微电网系统的固有特性ꎬ在通信数据传输过程中普遍存在ꎮ微电网中通信时延的存在阻碍了不同智能体之间的信息传递ꎬ也会引起扰动和不稳定[89]ꎮ微电网系统可以采用多种协议来实现电力系统与智能电子设备之间的高效通信ꎮ图4展示了微电网系统中通信网络的结构示意图ꎮ其中ꎬ通信基站是移动通信网络中最关键的基础设施ꎮ主要功能就是提供无线覆盖ꎬ即实现有线通信网络与无线终端之间的无线信号传输ꎬ保证数据收发信息的稳定性ꎮ通过传感器来获取信息ꎬ并将命令信号发送给分布式电源㊁储能设备㊁负载和开关等ꎮ信息接口采用面向对象的建模技术ꎬ利用可扩展标记语言(ExtensiblemarkuplanguageꎬXML)构建相应的信息模型ꎬ其信息交互符合IEC61850标准规约ꎬ通信架构扩展灵活ꎬ具有良好的开放性㊁互操作性以及设备特性自描述能力ꎬ主要用于监控㊁记录服务器㊁定期记录系统数据ꎮ采集到的电压㊁频率㊁有功㊁无功控制信号等数据通过分布在各层的路由器传送到微电网主控制中心ꎬ然后经过处理和决策将执行指令发送到执行单元[90]ꎮ微电网系统中分布式电源的稳定运行主要依赖于通信链路的可靠性ꎮ为了进行有效的能量管理和经济调度ꎬ就需要下层为提上层供参数信息ꎬ并接收来自上层的控制指令ꎮ因此ꎬ这种通信延迟可能是恒定的ꎬ也可能是随机的ꎬ随着分层控制和基于一致性控制在微电网系统中的应用ꎬ由低带宽通信引起的延迟问题引起了人们的注意[91]ꎮ时延主要分为固定通信时延和随机通信时延ꎮ固定通信时延有3种ꎬ一是发送时延ꎬ二是传输时延ꎬ三是处理时延ꎮ其中ꎬ接收和处理时延ꎬ取决于目标设备的软硬件性能ꎻ传输时延ꎬ主要依赖于通信网络带宽和传输距离ꎮ而随机时延主要是等待时延ꎬ由MAS层协议㊁连接类型和网络负载决定ꎮ在固定时延和随机时延条件下ꎬ如何保持微电网系统的稳定性是一个重要的问题ꎬ这是应用分层控制和MAS技术解决实际工程问题的主要难点[92]ꎮ331。

基于Multi-agent的机电设备群控智能调度系统

基于Multi-agent的机电设备群控智能调度系统
1 1 2
( 1. C olleg e of A u tom a tion , N anj ing U n ivers ity of T echnology , N a nj in g 210009, Ch ina ; 2. S ch ool of M echa n ica l an d P ow er E ng ineer ing , E ast Ch ina U n iversity S cience and T ech nology , S h ang h a i 200237, Ch ina ) Abstra ct: A group cont ro l syst em of m echat ronic equ ipm en ts has the charac teris tic s of dist ribu tion, para llel led w orking and indep endent operat ing. Th is p aper p ropo ses an in telligent d isp atching m odel w ith h ierarch ica l s t ructure based on m u lti2agen t theory and technology in orde r to op tim ize operat ing schem ing and to im p rove ope ra t ing effic iency. Som e key is sue s, such as the m ult i2agent system s tructure, agen t col2 labo ra t ion m echanism and cont ro l a lgor it hm , are s tudied in this p aper. A s i m u lat ion research about the g roup cont rol dispa tch ing fo r typ ical e leva to rs p rove s t ha t the m ethod is fea sib le , co rrect and ha s obviou s super io rity. Key words: m echa tronic equ ipm en t; m ult i2agen t system ; h ierarchica l s tructure; group con t ro l; op ti2 m al dispa tch ing

多Agent战术意图识别的知识组织与问题求解_曾鹏

多Agent战术意图识别的知识组织与问题求解_曾鹏
并且对于某个 Ag ent 实体 a 或者 类 T , 不 能同 属于上 层
抽象类 T i , T j : o, Ti(o)∨ T j(o), i ≠j 或 o, T(o) T i(o)∨ T j(o), i≠j
定义 T ac 为战术原则描 述(Doctrine)集 , C 为 CO A 集 , B 为单 A gent 的行动事件集 , HSA 为单 Ag ent 计划假 设 , H MA 为 多 A gent 计划假设 , HTA 为战术计划假设 , 于是有 :
Definitio n2 :PL =T ac ∪ C ∪ B iff B ∪ HSA O B ∪ C ∪ H MA O B ∪ C ∪ T ac ∪ H TA O B ∪ C ∪ T ac ∪ H / False 下面分别从单 A g ent 、多 A ge nt 以 及战 术 A g ent 等 不同 层次对 P L 中计划元素的建模与 组织展开详细分析 。 4.1 单 Agent 行动事件体系 从任务分解的角度而 言 , 战 场分布 的每个 A gent 都 担负 着一定的任务 , 任务 决定了 A gent 的 行为 , 行 为则反 映了 Ag ent 的意图 。 要识别 Ag ent 的行为意图就必须要建立起关于 该 A gent 的计划库 , 才能有根据 地建立 起相应 计划假 设来解 释 A gent 的意图 。 对 于不同 的 Ag ent, 由 于遂 行任 务的 差别 以及作战的方法模式各不相同 , 必须要针对性 的建立相应 Ag ent 的计划库 。 而战场中 分布 A g ent 的规 模可能是 庞大 的 , 实现每个 Ag ent 计划库的穷尽组织与建设将变得困难而不可 行 , 因此需要对大量的 Ag ent 实施聚类和分类 。 设战场实体空间 由分 布的 A gent 实 体 o 组 成 :A ={o1 , o2 , o, … , ok , … , o1}, 0<k <l 。 对分布的 A gent 进行分类 , 得到实体类型 T i : Classi f ier(A)={T i 0<i<n}

基于大规模新能源接入的配电网协调控制策略研究

基于大规模新能源接入的配电网协调控制策略研究

基于大规模新能源接入的配电网协调控制策略研究作者:李加亮毕京斌来源:《无线互联科技》2024年第13期作者简介:李加亮(1987—),男,工程师,本科;研究方向:电力系统,新能源承载力和消纳能力分析。

摘要:文章分析了新能源接入对配电网的影响,包括电压波动、功率不平衡、谐波污染等,提出一种基于模型预测控制的优化调度方法。

该方法通过滚动优化和反馈校正,实现新能源发电和配电网负荷的实时匹配,降低了配电网的运行成本。

对所提出的协调控制策略仿真验证,结果表明该策略能够有效地平抑新能源发电的波动,提高配电网的电压稳定性和供电可靠性。

关键词:大规模;新能源;配电网;控制中图分类号:TP393 文献标志码:A0 引言随着全球能源结构的转变和可持续发展战略的深入实施,新能源拥有相应的协调控制策略以提高配电网的运行效率和稳定性[1-2]。

本文基于大规模新能源接入的配电网协调控制策略,通过深入分析新能源接入对配电网的影响机理,提出一种有效的协调控制策略,旨在实现新能源发电单元、储能单元和负荷单元之间的优化协调,提高配电网的供电可靠性和电能质量。

同时,本文还将探讨新能源接入下的配电网优化调度问题,以期为实现配电网的绿色、智能、高效运行提供理论支撑和实践指导[3-4]。

1 新能源接入对配电网的影响新能源的接入显著优化了配电网的能源结构。

过去,配电网主要依赖化石能源,不仅资源有限,而且环境污染严重[5-6]。

如今,风能、太阳能等可再生能源的接入,使得配电网的能源来源更加多样化、清洁化。

这不仅有助于缓解能源紧张的局面,更在推动环保事业、实现绿色可持续发展方面发挥积极作用。

新能源的接入也给配电网运行带来一系列挑战。

由于新能源发电具有间歇性和波动性,这使得配电网的供电可靠性和电能质量面临新的考验。

为应对这些挑战,配电网需要采用更加先进的运行控制技术,实现对新能源发电的精细化调度和管理。

同时,还需要对现有设备与系统完成升级和改造,以提高配电网对新能源的接纳能力和运行效率。

多主体模拟技术简介

多主体模拟技术简介

多主体模拟技术简介多主体模拟(multi-agent simulation)是一种新兴的建模仿真技术,得到了各方面的关注,本章首先概述它的理论背景,而后简单介绍一种大型多主体微观模拟经济系统——Aspen,最后介绍在各种领域均取得了很好应用效果的通用多主体模拟软件平台——Swarm软件。

第一节多主体模拟的理论背景多主体模拟产生的理论背景是复杂适应系统理论的兴起和发展,它也是考察复杂适应系统的最主要手段。

一、复杂适应系统理论的由来复杂适应系统(Complex Adaptive System,简称CAS)产生于人们对复杂性的研究,而说到复杂系统的研究,就不能不提到圣达菲研究所(Santa Fe Institute)。

圣达菲研究所的创始人考恩(George Cowan)于1984年联合一大批各方面的专家对复杂性问题进行了讨论,包括诺贝尔经济学奖得主阿罗,诺贝尔物理学奖得主盖尔曼和安德森等等。

在此次会议上,各领域的专家找到共同的研究兴趣,也就是复杂系统。

在不同学科领域内均存在大量复杂系统,它们之间存在相当程度的相似性,然而以往还原论的科学研究思维难以对它们加以整体把握。

科学研究中存在的条块分割、缺少交流现象也使得人们难以综合各方面知识。

为此与会者一致同意设立圣达菲研究所,作为对复杂性的一个研究中心。

其特色是使各种差异极大的学科能开展共同研究,创建了一个包容性极强,不受传统的资金分配、成果认定体制约束的研究场所。

为此圣菲研究所吸引了全世界大量优秀的人才进入,从事短期的交流合作,成为新思想、新概念的发源点,而圣达菲研究所也在前不久被评为全美最优秀的5个研究所之一。

得益于这种研究环境,霍兰(J. Holland)于1994年圣达菲研究所成立10周年时的讨论会上首次提出了复杂适应系统的概念,他也是遗传算法(genetic algorithm)的创建者。

二、复杂适应系统的基本思想复杂适应系统的概念是从自然界和人类社会中各种复杂系统的观察而产生的一种概念,它的产生也得益于对以往科学研究实践中所遇到问题的反思。

多智能体编队问题的研究

多智能体编队问题的研究

引言:多智能体的协同在很多工程中具有广泛应用背景,如区域搜索、战场环境侦察、多战机协同作战、舰队协同作战、导弹突防、目标多点跟踪等[1]。

在执行不同的任务时,需要依据不同的场景实现不同的编队形态,既能够实现既定任务,又能够保证协同作战时的灵活性。

因此,对于多智能体的编队问题研究对于多智能体协同执行任务是有较大的意义的。

多智能体编队问题包括固定编队控制和时变编队控制,其中固定编队控制是时变编队控制的特例。

由于在实际问题中多智能体编队往往需要针对不同的任务场景采用不同的编队形式,如导弹突防时多智能体需要采用间距较小的编队形式,而在巡航阶段需要采用间距较大的编队形式,所以可以看出多智能体的时变编队研究具有更高的实用意义。

基于上述的多智能体时变编队的优点,本文重点研究多智能体时变编队的控制问题。

一、多智能体编队控制的现状和当前存在的问题针对多智能体编队的研究,目前对于固定编队的研究方法较为成熟,且研究成果较多。

比较常见的一种方法是基于人工势场方法的编队保持策略,即系统建立多智能体之间的人工势场,通过感知势场梯度的变化来给单个智能体的控制器一个控制量,进而给出单个智能体的运动方向和运动速度。

该方法要求多智能体系统之间具有通信能力,至少应该保证系统的通信拓扑能够生成一个以图论语言描述的有向生成树。

简单来说就是任何一个智能体的状态信息发生变化时都可以通过通信网络将信息传递至整个多智能体网络。

该方法被广泛的应用于“领导-跟随者”、“虚拟领航者”以及多智能体编队问题的研究【摘要】 无人机或无人车等装备是军工领域中常见的现代作战装备之一。

然而在很多作战环境下单一的无人作战装备难以完成复杂的军事任务,因此提出了多智能体协同作战的理念。

多智能体在执行任务时往往需要实现不同的预设编队,进而实现避障、减小雷达反射截面积等任务,因此多智能体编队控制问题便成为需要解决的核心问题。

多智能体编队控制问题有固定编队及时变编队等问题,时变编队显然更具有实际的工程意义。

multi agent算法代码

multi agent算法代码

多智能体系统(Multi-Agent Systems)是指由多个独立个体组成的系统,这些个体可以自主地进行行动并与其他个体进行交互。

在计算机科学领域中,多智能体系统是一个非常重要的研究领域,涉及到分布式人工智能、机器学习、优化算法等多个领域。

其中,多智能体算法(Multi-Agent Algorithms)是指针对多智能体系统设计的解决方案,用来协调和管理多个智能个体的行为,以达到某种特定的目标。

多智能体算法有许多种类,其中multi-agent算法是其中的一种。

下面将介绍multi-agent算法的相关内容,并给出其代码实现。

1. 算法简介multi-agent算法是一种涉及多个个体协同完成任务的算法,个体之间可以进行相互通信和交互。

该算法通常用于解决分布式决策、协同控制、资源分配等问题。

在实际应用中,multi-agent算法被广泛应用于智能交通系统、无人机协同控制、物联网等领域。

2. 算法实现以下是一个简单的multi-agent算法的伪代码实现:```// 初始化智能体Agent[] agents = new Agent[n];for (int i = 0; i < n; i++) {agents[i] = new Agent();}// 模拟多轮协同决策for (int round = 0; round < maxRound; round++) { // 智能体相互通信for (int i = 0; i < n; i++) {for (int j = 0; j < n; j++) {if (i != j) {agents[i]municate(agents[j]);}}}// 智能体协同决策for (int i = 0; i < n; i++) {agents[i].cooperate();}}// 输出最终结果for (int i = 0; i < n; i++) {System.out.println(agents[i].getResult());}```以上伪代码实现了一个简单的multi-agent算法,其中包括了智能体的初始化、多轮协同决策的模拟、智能体之间的通信和协同决策过程。

生态系统英语文献

生态系统英语文献

Modeling and Simulation of Ecosystem Based on Multi-AgentSystemZhen Li, Guojian Cheng, Xinjian QiangSchool of Computer ScienceXi’an Shiyou UniversityXi’an, Shaanxi ProvinceChinazhli@, gjcheng@, qiangxj@Abstract: - Based on the complex adaptive system theory and the multi-agent modeling and simulation method,a reactive agent model of ecosystem is built in this paper. The model tries to naturally show the general characteristics of grassland ecosystem while retained its eco-adaptive complexity and diversity. In experiment, the model is implemented on the Net Logo which is a multi-agent simulation platform. By adjusting environmental factors and various parameters, a variety of real phenomenon of a grassland ecosystem is emerged. Finally, the simulation results are analyzed and compared them with the actual ecosystem showing the correctness and expansibility of this model.Key-Words: - Multi-Agent System, Net Logo, Ecosystem, Complex Adaptive System, Modeling, Simulation1 IntroductionComplex Adaptive System, put forward firstly by the American computer scientists Holland in 1994. The basic idea is: members of CAS can constantly interact with system environment and other subjects and continuously "learning" and "experience" in this process, according to these experiences to change their structure and behavior, thus emphases the new structure , phenomenon and more complex behavior in the overall level . Complex Adaptive System's complexity stems from its main body of adaptability, namely “establishment of complex adaptive”[1]. Actually, the eco-system is a large and complex system. The Eco-system consists of a large number of populations which have nonlinear interactions between each other, these populations constitute a hierarchical system and the low-level’s disorder on the local behavior of various groups may have shown a high level of order behavior patterns; In order to survive, the various groups of ecosystems must adapt to the changing environment; All species in the ecosystem have their own information, and problem solving skills; There are also no direct order to teach each specifics ’s behavior of the various global control institutions in ecological system ; The behavior of each species is asynchronous concurrent in ecosystem. The mentioned systems nonlinear, hierarchical,self-organizing, adaptive, decentralized control, asynchronous concurrency and other characteristics, is the characteristics of complex adaptive systems. Therefore, the ecosystem is a typical time of evolving complex adaptive systems. For such complex systems, using mathematical2 Multi-Agent system’s architecture and features2.1 Agent’s architecture and featuresThe so-called Agent is a physical or abstract entity, it can act on their own and the environment, and to respond to the environment. Generally speaking, Agent has the knowledge, goals and capabilities. Knowledge refers to the Agent on its surroundings or it required description of the problem’s solution;goals refer to all acts taken by the Agent aregoal-oriented; capacity refers to the Agent with the reasoning, decision-making, planning and control capabilities.2.2 Multi-Agent SystemMulti-Agent System (MAS) is a collection which composed by multiple calculable Agent .The MAS modeling simulation is a modeling simulation method based on the object-oriented and bottom uptechnology. It uses various Agent attributes and behaviors in MAS, the interactions between the individual of simulation component system and individual, through individual attributes and behaviors with integral attributes and behaviors to feedback and correction and to study the development and evolutionary of the system [2-5].MAS have general characteristics as follows [1]:1. Each Agent has only the partial or incomplete information or with only part of capabilities to solving problem, so each Agent’s vision is limited.2. MAS without a global control system.3. Data is scattered processing and storage.4. Can realize asynchronous calculation.The above features of MAS just correspond to the generated autonomy, distribution, and parallelism features of the ecosystem. Therefore, the introduction of MAS can be used for simulation, optimization, simulation and control ecosystem. It has also become an important method and means to research the ecological system.MAS modeling’s major concepts: through the changes way of designated Agent internal state, to observe a large number of mutual Agent interaction’s results, and trying to find out emerged phenomenon in the complex environment; the global control are not present in the process of modeling and simulation, For a specific certain Agent is set simple attributes and behaviors, and then through other Agent or environmental interaction to evolve the system.3 The Multi Agent Model of Ecological system3.1 The Multi Agent Model of Ecological systemThis model mainly consists of inorganic environment Agent, producers, consumers Agent. The model constituted a local environment of ecological system. The local environment is two-dimensional, classified as composed grid of Agent producers, each producer Agent have a small piece of rectangular area, it is can't move. Agent Consumer can move freely andproduce the relevant behavior in the area constitute by Agent producers. Inorganic environment agent as a global Agent, it watched producers Agent and consumer Agent constitute local environment, can obtain the instruction execution outside all or part of the state, or controlled the outside world. It can still change the outside world at different degree to simulate real outside all sorts of different environment’s impact on the region.3.2 All kinds of Agent ModelingBased on MAS model, the Agent is behavior corpus, on certain condition, it can response to some external events, namely in some activities, making the state transition or produce new events. Agent has level-oriented, simultaneity, it can use triad description: <attribute,structure, behavior>. For the ecological complex adaptive system model of the Agent, attributes can be divided into three categories. They are the space attributes, physical properties, and the other attributes. Structure refers to the internal Agent contained hypo-Agent or entity set, the structure of the hypo-Agent or entity attributes, and the Agents attributes of its internal state jointly characterized. Agent actions are divided into reactive behavior and adaptability behavior. Reactive behavior refers tothe Agent on its environment conditions, internal state and other driver response. Adaptive behavior refers to the Agent from environmental and interaction with other Agent in the process of learning and accumulate experience, and improve their adaptability to fit the environment activities. This model Agent in the main internal structure is a reactive Agent. In order to stimulate the way, a response to external the environment is needed to respond to changes, thus realize the ecological system evolution and propulsion.1. Inorganic environmental Agent: through different environments, set the intensity of photosynthesis, the producers and the fitness degree of the consumers Agents, to achieve a real return based on the external environment.2. Producers Agent: First of all to acquire the behavior of inorganic environment of the Agent to carry out their actions. It has some static properties and it does not change in Agent's life cycle. Producer Agent's static properties: self-energy (Energy), growth time, position (x, y), color (Color). Producer Agent behavior: growth, according to the conditions of inorganic environment to observe whether theycan growth and the growth rate.3. The Consumer Agent: definite the two kinds of consumers Agents, herbivorous Consumer Agent and carnivorous consumers Agent. The Herbivorous consumers eat producers Agent to get energy and the carnivorous Agent eats herbivorous Agent to get energy. They have available activities on certain regional, and if the mortality of consumers Agent excess in this region so inorganic environment by Agent producers will increase the impact on growth.The consumers Agent static properties: Energy, Reproduce, Energy consume, Gain, Energy max, Range. Consumer Agent’s behavior:(1) Foraging rules: the consumer Agent do activities within certain regions, they find food through close interaction with each other but were different Agent. The herbivorous Agent and Producer Agent interact with each other for feeding. The carnivorous Agent and herbivorous Agent interact with each other for food.(2) Breeding rules: the consumer Agent through the reproduction rate to breeding which set by inorganic environment, the inorganic environment of Agent through reality conditions to set corresponding reproduction rate.(3) Death rules: all kinds of Agent’s energy less than0 or be established, then death.4 Model Simulation4.1 Many Agent Simulation Platform NetLogoNet Logo is a multi-Agent modeling and simulation integrated environment, especially suitable for the modeling and simulation of complex systems with time evolution. Net Logo is developed by the Northwestern University to connect Center for Connected Learning and Computer-Based Modeling, CCL. The objective is to provide powerful CAD tools for research and education [8]. Net Logo modeling assumptions: the space is divided into grid, each grid is a static Agent, the Mobile Agents located in two-dimensional space, each Agent have independent action, all of the main body doing parallel and asynchronous update, the entire system as time and dynamic changes. Net Logo defines the three types of Agent: territory, Turtle, Observer. The Patch which is static Agent, it divided our virtual space into grids, Turtle is a dynamic Agent, it can also be a variety, it is moving in the Patch randomly,you can interact with each other and interacted with Patch. The Observer as a global Agent observed the local world which composited by the Turtle and the Patch, and control the environment.4.2 Simulation Model and Results Analysis4.2.1 Simulation 1Inorganic environment ecosystem model requires relatively abundant photosynthesis and water conditions. First set all kinds of Agent in photosynthesis and enough moisture case attributes. Plant Agent’s properties:(1) Obtain photosynthesis fully.(2) Plant Agent’s growth rate: Grass growth = 9animals Agent’s properties.(3) Number of initial population: Initial Sheep Number = 99, Initial Wolf Number = 49(4) Its own energy: Initial Energy randomlygenerated between 1 and 4.(5) The energy consumption: each move of Energy Consume = 1.(6) Range: Range = 3.(7) Access to energy: Sheep Gain = 5, Wolf Gain = 15.(8) Reproduction rate: Sheep Reproduce = 3%, Wolf Reproduce = 5%.4.2.2 Simulation 2Put environment into water is not adequate, then the survival rate of plants and biological become low. Other conditions are the same as Simulation 1, only put the environment into arid environment, photosynthesis and water is not sufficient, plant growth rate become low.4.2.3 Simulation 3Conditions and simulation experiments 2 are identical, increasing the fitness of the biologicalcommunity to the environment.(1) The access to energy: Sheep Gain = 8, Wolf Gain = 30.(2) The reproductive rate: Sheep Reproduce = 6%,Wolf Reproduce = 10%. From the above simulations show, when the population increased the fitness for environment, the fluctuations is high in various types of population at the beginning, and then have some down, but gradually adapt to the environment, the consumers population are becoming more and more balanced. This is show that the poor ecological environments are not suitable for the survival of biological communities. Some species will be eliminated, if it cannot adapt to the biological environment, and the number will decline. However, when the populations adapt to the environment, the population size will gradually stabilized, will not lead to extinction of biological communities in a population, over time, will achieve the ecological balance. This feature is fully consistent with the natural survival of the fittest, the law of survival of the fittest.5 ConclusionThis paper based on the theory of complex adaptive systems, through a multi-Agent modeling method, to establish a grassland ecosystem model based on reaction type Agent. The model thought the properties and behavior of various types of Agent to feedback and to complete the entire eco-system evolution and progress. Then in the Net Logo simulation platform, by changing the environmental factors andattribute parameters of various types of Agent which observed the changes in population, especially the population which has high adaptability in harsh environments, the low fitness wereeliminated or even be extinct, reflecting the characteristics of an ecosystem. Extension of the model is high, in the case of environmental change, just by different populations of Agent to change the properties and behavior can be simulated different ecosystems, such as marine ecosystems. However, the text of the Agent only defines the behavior of reactive, not adaptive, so I hope you can join in the follow-up to all kinds of Agent's adaptive behavior to improve the system and make it to become a more realistic ecosystem, ultimately to provide services for the interaction between humans and the environment and ecological harmony.AcknowledgementsThis paper is supported by the National Natural Science Foundation of China (40872087) and the Shaanxi Provincial Key Subjects (Computer Application Technology).References:[1] Jiao Licheng, Liu Jing, Zhong Wecai and so on. Coevolutionary Computation and Multi-agent System. Beijing: Science Press. 2006, 34 ~ 51.[2] Shi Zhongzhi. Intelligent Agent and Its Application[M]. Beijing: Science Press. 2000.[3] Yu Jiangtao. The research and application of Multi-agent model, learning, and collaborative[PhD thesis]. Hangzhou: Zhejiang University.2003.[4] Potter MA , De Jong KA. Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evolutionary Compution. 2000, 8(1):1~29.[5] WEI Yun,HAN Yin,Fan Bing-quan. Modeling and simulation of intersection based on multi-agents and fuzzy control strategy. Journal of University of Shanghai for Science and Technology. 2010, Vol. 32, No. 3, 259~262.[6] Jian-Chao Zeng, Hong-Gang Wang. Modeling and Simulation of the food Chain Based on Complex Adaptive System. 2009.[7] Cheng J,Liu H,Zhang H.Primary study on MAS-based single-species ecosystem model. SPCA 2006: 2006 1st International Symposium on Pervasive Computing and Applications. United States: Computer Society, 2007.专业英语课程大作业题目:Modeling and Simulation of EcosystemBased on Multi-Agent System院系:电子信息与电气工程学院学生姓名:杨锐学号:201002070007专业班级:电子信息工程(10级专升本)2011年10月22 日。

船运术语缩写

船运术语缩写

ABT about 大约ABV above 以上ACCT account 由...承担,租船人,租家ADV advise 通知ADD address 地址AFMT after fix main terms主要条款定下之后AGT agent代理ARBI arbitration 仲裁BBB before breaking bulk 开舱卸货前B/DAYS banking days 银行工作日BENDS.BE both ends 指装卸两头〔港〕BGD bagged 袋装的BLK bulk 散装〔货〕BS/L bills of lading 提单BSS 1/1 bases 以一个装港一个卸港为基准BW AD brackish water arrival draft 船到卸港的吃水CHOPT charterers’ option 租家选择CHRTS charterers 租船人,租家CO charterers option 租家选择COMM commission 佣金COUNTER 还盘C/P charter party 租约C.Q.D customary quick dispatch 按港口习惯速度尽快装卸CVS consecutive voyageDFD demurrage/despatch 滞期/速遣DHD demurrage/free despatchDOP droppint outward pilotDRRK derrick 吊杆DWA T deadweight all toldDWCC deadweight cargo capacityEIU even if used 即使使用ETA expected time of arrival 预抵时间ETD expected time of departure 预离时间FHINC Fridays and holidays includedFIO free in & free out 船东不负责装/卸费FIOST free in,out, stowed〔理舱〕trimmed 〔平舱〕FILO free in , liner out 船东不负责装,但负责卸FLT full liner term 全班轮条款,即船东负责装/卸费FLWS follows 以下FRT freight 运费FWAD fresh water arrival draftGENCON,GCN 金康合同GROSS TERMS liner termsIAC including Address Commission〔租家佣金〕IACS international association of classification societies 国际船级社LIFO liner in, free out 船东负责装,但不负责卸L YCN laycan laytime and cancel timeL/D loading/discharging 装/卸MIN 至少FLUSIT TWNMOLOO more or less at owners option 增减由船东选择NAABSA more or less at charterers option 增减由租家选择NOR notice of readiness船舶备安通知NOM nominated 指定的OAP over age premium 船的超龄保险OFFER 报盘OO owners option 船东选择OPTN option 选择OWRS owners 船东PART CGO part cargo 拼装P & I CLUB protection and indemnity associations 船东互保协会PPT prompt 即装,马上要装PMT per Metric Ton 每公吨PWWD per weather working day 每晴天工作日RCVRS receivers 收货人SBA safe berth anchor 安全锚地区性泊位SBP safe berth port 安全泊位安全港SDBC,SD single deck 一层舱统舱〔船〕S/F stowage Factor 积载因素SHEX Sunday holiday Except 星期天,节日不包括在内SHIPPER 发货人SPOT indicates that a ship or a cargo isimmediately available.STEM refers to the readiness of cargo and is often a prerequisite tothe fixing of a vessel, eg. Subject stem subject to the cargoavailability on the required dates of shipment being confirmed.SURVEYORS 验船师SWL safe working load.SW AD salt water arrival draftSUB subject 以…为条件TD,TWN tweendeck 二层舱〔船〕TIP taking inward pilotTNNG tonnage 指船TTL totalUU unless used 除非使用WIBON whether in berth or not 不管靠泊与否WIFPON whether in free pratique or not 不管船舶检验与否WIJION whether in joint inspection or not 不管船舶联检与否WIPON whether in port or not 不管抵港与否W/M weight or neasureWOG without guarantee 不保证WP weather pernittingWTS working time saved 按节约的工作时间计算WWDSHINC weather working days Sundays and holidays included晴天工作日,包括星期天及节假日W.W.W.W WIPON,WIBON,WIFPON,WIJIONYRS years 年BLT Built建造年份CALL SIGN: 呼号CBM: 立方米CLASS: 船级DRAFT 吃水DWT: Dead Weight 载重吨FLG: 船旗G/B: 散/包装舱容GRT/NRT: 总登记吨/净登记吨HH: hatch舱口hold舱APS arrival pilot station 到达引航站ATDNSHINE actual time of dispatchBOD bunker on deliveryBOR bunker on redeliveryBallast Water 压舱水Beaufort Scale 蒲福风级3级CONS consumption 消耗耗油Douglas state of sea 3 道式海浪3级DTLS detailsDLOSP 到达引航站DEL delivery 交船deratting certificate 恶意鼠证书ENT/VICT 给船长用于招待的费用FW fresh waterGMT 国际标准时H.cover hatch coverH+M value 船的价值ILOHC 还船时给船员的扫舱费NIL nothing 无IWL institute warranty limitsIFO industrial fuel oil 燃料油KTS knots节Laden 载满MDO 船用柴油marine diesel oil oilMGO marine gas oilNYPE 纽约土产期租船合同P/F prefer宁可,优先RDEL redelivery还船ROB remaining on boardSPD speed 航速—————————————以下可能有重复——————————————AA Always afloat 经常漂浮AA Always accessible 经常进入AA Average adjusters 海损理算师AAR Against all risks 承保一切险AB Able bodied seamen 一级水手AB Average bond 海损分担证书A/B AKtiebolaget (瑞典)股份公司A/B Abean 正横ABS American Bureau of Shipping 美国船级协会ABT About 大约ABB Abbreviation 缩略语A/C,ACCT Account 帐目AC Alter couse 改变航向AC Account current 活期存款,来往帐户AC Alernating current 交流电ACC Accepted; acceptance 接受,同意ACC.L Accommodation ladder 舷梯A.&C.P. Anchor & chains proved 锚及锚链试验合格ACV Air cushion vehicle 气垫船ACDGL Y Accordingly 遵照AD Anno Domini 公元后AD After draft 后吃水ADD Address 地址ADDCOM Address commission 租船佣金ADF Automatic direction finder 自动测向仪AD V AL Ad valorm 从价〔运费〕ADV Advise;advice; advance 告知;忠告;预支A/E Auxiliary engine 辅机AF Advanced freight 预付运费AFAC As fast as can 尽可能快地AF Agency fee 代理费AFP Agence France press 法新社AFS As follows 如下AFT After 在。

基于多agent的离散制造业制造执行系统框架研究

基于多agent的离散制造业制造执行系统框架研究

收稿日期:2008203201;修回日期:2008205212作者简介:潘颖(19772),女,黑龙江双鸭山人,博士研究生,主要研究方向为制造执行系统、多agent 系统、精益生产等(panying@yahoo .cn );张文孝(19632),男,教授,博士,主要研究方向为机械强度.基于多agent 的离散制造业制造执行系统框架研究潘 颖,张文孝(大连水产学院机械学院,辽宁大连116023)摘 要:针对离散制造业的特点对离散制造业制造执行系统的功能模块进行了分类。

构造了对应的agent 模块,以此提出一种基于多agent 的分布式离散制造业制造执行系统体系结构,并研究了其中每个agent 的运作机制与多agent 之间的交互协调机制。

特别是构造了策略agent,它与调度agent 协同,可更好地实现离散制造业制造执行系统功能的合理、实时调度。

这种结构非常有利于离散制造业制造执行系统的管理和控制,具有较好的可集成性、可扩展性和可重构性。

关键词:离散制造业;制造执行系统;多代理;体系结构中图分类号:TP303 文献标志码:A 文章编号:100123695(2009)0120244203Research on multi 2agent 2based MES structure of discrete manufacturing industryP AN Ying,Z HANGW en 2xiao(M echanical Institute,D alian Fisheries U niversity,D alian L iaoning 116023,China )Abstract:A i m ing at discrete manufacturing industry ’s characteristics,the functi on modules of its manufacturing executi onsyste m (MES )was classified,and corres ponding agent modules were built .A multi 2agent 2based MES structure of discrete manufacturing industry was p r oposed .Moreover,researches were made on the circulating mechanis m of each agent and the communicati on a mong multi 2agents .Es pecially strategy agent was constructed t o cooperate with schedule agent s o as t o realize l ogical and real 2ti m e scheduling .Such structure makes f or managing and contr olling the discrete manufacturing industryMES,and has integrati on,expansibility and reconfigurability .Key words:discrete manufacturing industry;M ES;multi 2agent;structure 随着市场竞争的进一步加剧,离散制造业的产品生命周期缩短,出现多品种小批量的生产形态,市场变化快难以预测。

基于Multi_agents系统的分布式数据挖掘

基于Multi_agents系统的分布式数据挖掘

3)本课题得到国家自然科学基金项目(60473113)、国家自然科学基金重点项目(60533080)资助。

庄 艳 硕士研究生,主要研究领域为分布式虚拟环境、Agent 技术;陈继明 博士研究生,主要研究领域为XML 、分布式虚拟环境;徐 丹 硕士研究生,主要研究领域为分布式虚拟环境、Agent 技术;潘金贵 教授,博士生导师,主要研究领域为多媒体信息处理、多媒体远程教育系统。

计算机科学2007Vol 134№112基于Multi 2agents 系统的分布式数据挖掘3)庄 艳 陈继明 徐 丹 潘金贵(南京大学计算机软件新技术国家重点实验室 南京210093)摘 要 计算机网络的发展以及海量数据的分布式存储,滋生了分布式数据挖掘(DDM )这一新的数据挖掘方式。

本文针对多agent 系统下的分布式数据挖掘进行了初步的研究,对agent 方法用于DDM 的优势、基于agents 的分布式数据挖掘的问题,以及典型的基于agent 的分布式数据挖掘系统和该领域的进一步研究方向作了一个概要的综述。

关键词 数据挖掘,分布式数据挖掘,基于多agent 系统的分布式挖掘 Distributed Data Mining B ased on Multi 2agent SystemZHUAN G Yan CH EN Ji 2Ming XU Dan PAN Jin 2Gui(State Key Lab for Novel Software Technology ,Nanjing University ,Nanjing 210093)Abstract The development of network and the storage of huge data in a distributed way bring on the distributed data mining (DDM ).The article gives a primary study focus on the Distributed Data Mining Based on Multi 2agent system.We summarize the advantages of agents for DDM ,problems in the agent 2based system for distributed data mining ,and some representative agent 2based Distributed Data Mining systems ,at last ,the f uture work of the area.K eyw ords Data mining ,Distributed data mining ,Data mining based on multi 2agent system 数据挖掘是用于在大规模数据集中获取感兴趣知识的过程。

WhyMulti-agentsystem为什么构造多智能体系统

WhyMulti-agentsystem为什么构造多智能体系统
Flock
mates outside this local neighborhood are ignored. 超出该agent的区could be considered a model of limited perception (as by fish in murky water) 区域可以认为是agent所具有的感知能力
这些复杂的情况需要处理巨量的数据,而数据又存在于不同的地方,
所有这些超出了传统的集中型计算能够解决问题的能力
Why multiagent system?-2
They
have the capacity to play an important role in developing and analyzing models and theories of interactivity in human societies, and solving problems which it is difficult to solve in conventional method.
3 rules: Separation 三条规则:1.分离

Separation: steer to avoid crowding local flockmates 尽量避免在本地汇集大量agent 个体
3 rules: Alignment 三条规则:2.保持群体形状
Alignment:
steer towards the average heading of local
flockmates 尽量使单体朝着各方向平均的趋势排列,尽力在各个方 向排成直线
3 rules: Cohesion 三条规则:3.适当的聚合
Cohesion:
steer to move toward the average position of local flockmates 单体群应围绕着中心移动,不能分离太远

未知异构多智能体系统无模型自适应动态规划同步控制-214

未知异构多智能体系统无模型自适应动态规划同步控制-214

Synchronization control of unknown heterogeneous multi-agent system via model-free adaptive dynamic programming
XIA Lina1, LI Qing1, SONG Ruizhuo1, WANG Zihan1, XU Zhen2
收稿日期:2021−09−22;修回日期:2021−11−09 通信作者:宋睿卓,ruizhuosong@ 基 金 项 目 : 国 家 自 然 科 学 基 金 资 助 项 目 ( No.61873300 , No.61722312 ); 中 央 高 校 基 本 科 研 业 务 费 专 项 资 金 项 目 (No.FRF-MP-20-11,No.FRF-IDRY-20-030)
第 3 卷第 4 期 2021 年 12 月
智能科学与技术学报
Chinese Journal of Intelligent Science and Technology
Vol.3 No.4 December 2021
未知异构多智能体系统无模型自适应动态规划同步控制
夏丽娜 1,李擎 1,宋睿卓 1,王子涵 1,许镇 2
则观测误差以指数趋向于 0。
证明 令 S = col(S1,", SN ) 、H = L + B ,式(4) 可以写为 S = −ρ(H ⊗ I)S ,在假设 1 下,H 所有的
Foundation Items: The National Natural Science Foundation of China (No.61873300, No.61722312), The Fundamental Research

随机拓扑下离散多智能体事件触发一致性

随机拓扑下离散多智能体事件触发一致性

随机拓扑下离散多智能体事件触发一致性作者:赵阳解静曹洒来源:《青岛大学学报(工程技术版)》2024年第01期摘要:針对离散时间多智能体跟踪不稳定的问题,本文研究离散多智能体系统的事件触发一致性控制问题,通过马尔可夫跳变拓扑结构实现各智能体间的信息交互,设计了一种基于动态响应的事件触发条件,给出了马尔可夫跳变控制协议,构造带有转移概率的离散Lyapunov函数,得到所有智能体是均方一致性的充分条件。

数值算例验证了所提方法的有效性,证明了本结论可用于解决随机拓扑下离散多智能体的跟踪不一致问题。

关键词:离散多智能体系统;随机切换拓扑;马尔可夫链;事件触发;均方一致性中图分类号: TP13文献标识码: A离散多智能体具有自主性强、距离范围内的容错率高、抗干扰能力强、系统强耦合及强不确定性等特征[1],适用于描述机器人协调技术及群集运动等[2-7]实际工程问题。

对于多智能体系统的拓扑结构,张圆圆等人[8]研究了无向联通拓扑结构图下的多智能体系统;尉晶波等人[9]解决了拓扑切换下的多智能体协同输出调节问题。

但有关离散多智能体系统的文献大多集中在固定拓扑和切换拓扑上[10-13],随机切换拓扑结构的成果较少。

随机切换拓扑结构能够更直观地表示智能体之间的信息交换问题,因此本文将重点考虑基于马尔可夫链的随机切换拓扑结构[14-15]。

事件触发控制在资源节约方面具有显著优势,可有效减少通信次数。

陈侠等人[16]使用动态事件触发机制研究了网络攻击一致性问题;XIE D S等人[17]研究了具有事件触发策略的领导者-追随者一致性控制;XUE S S等人[18]研究了分布式事件触发一致性问题。

目前在马尔可夫跳变拓扑条件下的事件触发结果并不多,还有许多问题需要研究。

基于此,本文考虑马尔可夫跳变拓扑下离散多智能体系统的事件触发一致性问题,利用线性矩阵不等式技术[19]给出均方一致性的充分条件,避免事件触发时间序列对邻域内其他智能体信息的持续监控,并说明如何避免Zeno现象,通过数值算例验证了所提方法的有效性。

网络化时滞多智能体系统的结构能控性研究

网络化时滞多智能体系统的结构能控性研究

网络化时滞多智能体系统的结构能控性研究摘要网络化多智能体系统的能控性已经成为了当下的热门问题,具有一定的理论价值和实际意义。

本文在先前成果的基础上,结合图论、控制理论、矩阵理论等方面的书籍和相应的数学软件,从代数和图形的角度出发主要研究了具有单时滞和多时滞的多智能体系统的完全能控与结构能控。

主要有如下三个方面:1.研究了基于绝对协议下具有时滞的离散时间多智能体系统的完全能控性和结构能控性。

分别建立了单时滞和多时滞的模型,并将他们转化为等价无时滞的增广系统,并证明了这个维数较高的无时滞的增广系统与一个维数较低的子系统是等价,从而得到了相应系统下的完全能控性和结构能控性的一些新的充分必要的代数条件和图条件。

同时给出了举例并进行仿真,对相应的结果进行了验证。

2.研究了具有时滞的相对协议下某些特殊拓扑结构的多智能体系统的能控性。

建立了一些特殊拓扑的系统能控性的充要条件,通过研究能控性的充要条件与特殊拓扑的关系,并提出了一个适用的多智能体系统的能控性算法,用该算法测试了这些特殊拓扑能控性,并得到了不同拓扑下的系统能控性的结论。

3.研究了基于相对协议下的离散时间多智能体系统的结构能控性。

建立了相对协议下的单时滞模型与多时滞模型,得到了一个等价无时滞的增广系统,并将其转化为一个与原系统等价且维数更低的系统,在此基础上讨论了其结构能控性。

并用相应的算例对理论结果进行了验证。

关键词:多智能体系统,完全能控,结构能控,绝对协议,相对协议,时滞Research on structural controllability of networked multi-agent systems with time-delaysAbstractThe controllability of networked multi-agent systems has become a hot issue and has certain theoretical value and practical significance. On the basis of previous achievements. Through graph theory, control theory, matrix theory and corresponding mathematical software, this paper mainly studies the complete controllability and structural controllability of multi-agent systems with single time-delays model and multiple time-delays model from the perspectives of algebra and graphics. There are three main aspects as follows:1. The complete controllability and structural controllability of discrete-time multi-agent systems based on absolute protocol with time-delays are studied. We establish a single time-delays model and multiple time-delays model respectively, and transform them into an equivalent augmented system without time-delays, and prove that the augmented system of high dimension without time-delays is equivalent to a subsystem of low dimension, thus some new sufficient and necessary conditions of algebraic and graphical are obtained for complete controllability and structural controllability of the corresponding systems, which also verifies the complete controllability and structural controllability of the original system. At the same time, the numerical examples are given and the corresponding results are verified by simulation.2. The controllability of multi-agent systems of some special topologies based on relative protocols with time-delays is studied. The necessary and sufficient conditions for the controllability of some special topologies are established. By studying the relationship between controllability conditions and special topologies, an applicable controllability algorithm of multi-agent systems is proposed, which is used to test the controllability of these special topologies, and the conclusions of controllability under different topologies are obtained.3. The structural controllability of discrete-time multi-agent systems based onrelative protocol is studied. The single time-delays model and multiple time-delays model under relative protocol are established. And an equivalent augmented system without time-delays is obtained, which is transformed into a system of lower dimension. And this system is equivalent to the original system. On this basis, the structural controllability of the system is discussed. The corresponding numerical examples are used to verify the theoretical results.Key words: Multi-agent systems,complete controllable,structural controllable,absolute protocol,relative protocol,time-delay目录摘要 (I)ABSTRACT............................................... I I 第一章绪论 (1)1.1 多智能体系统的研究背景及意义 (1)1.2 多智能体系统能控性的研究现状 (2)1.3 研究内容 (4)第二章基本概念和主要引理 (5)2.1 代数图论 (5)2.2 控制理论 (6)2.2.1 完全能控 (6)2.2.2 结构能控 (6)2.3 矩阵理论 (7)第三章基于绝对协议的离散时间多智能体系统的能控性 (9)3.1 模型 (9)3.1.1 单时滞模型 (9)3.1.2 多时滞模型 (10)3.2 具有单时滞的离散时间多智能体系统的能控性 (11)3.2.1 完全能控 (11)3.2.2 结构能控 (15)3.2.3 举例与仿真 (16)3.3 具有多时滞的离散时间多智能体系统的能控性 (19)3.3.1 完全能控 (19)3.3.2 结构能控 (21)3.3.3 举例与仿真 (21)3.4 本章小结 (23)第四章具有时滞的某些特殊拓扑的能控性 (24)4.1 模型 (24)4.2 主要结论 (25)4.3 一些特殊的拓扑结构 (26)4.4 本章小结 (31)第五章基于相对协议的离散时间多智能体系统的结构能控性 (32)5.1 模型 (32)5.1.1 单时滞模型 (32)5.1.2 多时滞模型 (33)5.2 单时滞模型的结构能控性 (34)5.3 多时滞模型的结构能控性 (36)5.4 特殊情况下的结构能控性 (37)5.5 举例与仿真 (38)5.5.1 单时滞算例 (38)5.5.2 多时滞算例 (41)5.6 本章小结 (44)第六章结论与展望 (45)6.1 主要结论 (45)6.2 未来展望 (45)参考文献 (47)在学期间的研究成果 (52)致谢 (53)第一章绪论1.1 多智能体系统的研究背景及意义智能体集群行为是人类向自然界不断地学习,对自然界的观察与模仿,从而引发出来的灵感,例如前人模鸟类制造出了飞机,模仿海豚制造出了潜艇,模仿人脑制造出智能机器人,以及模仿生命的协同与进化克隆出个体等等,但是这些仅仅都是单个的个体,且有些任务单个个体很难完成,这就需要多个智能体之间的配合与协调,而这些多智能体组成的集合形成多智能体系统。

Learning Aspect

Learning Aspect

The Learning Aspect PatternAlessandro Garcia Uirá Kulesza José SardinhaCarlos Lucena Ruy MilidiúSoftware Engineering LaboratoryPontifical Catholic University of Rio de Janeiro - PUC-Rio - Brazil{afgarcia, uira, sardinha, lucena, milidiu}@inf.puc-rio.brLearning AspectAn intelligent agent has the ability to learn and adapt itself as a result ofseveral events, including its own actions, its mistakes, its successiveinteractions with the external world, and collaborations with other agents. Asthe agents’ complexity increases, object-oriented abstractions cannotmodularize the learning-specific concerns that tend to spread across severalclasses of the agent design. The Learning Aspect pattern documents anaspect-oriented solution for the modularization of the learning concerns. Thepattern totally decouples the basic agent structure from the learning protocol,which in turn improves the system reusability and maintainability.Keywords Machine Learning, Aspect-Oriented Software Development, Intelligent Agents. Context Agents need to learn based on internal and external events, including their own actions, their mistakes, the successive interactions with the externalworld and the collaborations with other agents [1, 2, 3]. The introduction ofthe learning property to an agent design is typically based on the use ofmachine learning techniques [2, 3]. Hence engineers of intelligent agents [2, 3,4] must deal with the agents’ basic functionality, the agent services that aremade available to the clients, and with a number of learning-specificconcerns, which greatly increase the system complexity.Many learning facets need to be considered [2, 3, 4], including the definition ofevents that trigger the agent learning, the information gathering to enable thelearning process, the specification of the learning knowledge, theimplementation of the learning algorithms to process the gatheredinformation, and the adaptation of the current agent knowledge. In thiscontext, the separation of the learning concerns is crucial to make the agentcomponents easier to maintain and reuse.Example Consider a multi-agent system that supports the management of paper submissions for conferences as well as the reviewing process. This system isfrom herein referred to as Expert Committee (EC). The EC systemencompasses user agents that are software assistants to represent systemusers in reviewing processes. The basic functionality of the user agents is toinfer and keep information about the corresponding users related to theirresearch interests and their participation in scientific events.In addition to their basic functionality, user agents can collaborate with eachother; the collaboration concern comprises the roles[26, 27] played by theagents. Each role represents collaborative activities in specific contexts. EachEC agent plays different roles, but the main ones are chair and reviewer. Rolesare associated with plans, which implement more sophisticated collaborativeactivities. The chair role has plans for distributing review proposals; thereviewer role has plans for judging the chair proposals. The chair negotiateswith reviewers for performing reviews. Figure 1 shows classes representing theagents’ basic functionalities and some examples of roles and plans; it does notaddress the learning-specific concerns.Figure 1. Object-Oriented Design for the User Agents and their Roles (Without Learning) EC agents also incorporate the learning property, using two widely-appliedlearning techniques: Temporal Difference Learning (TD-Learning) [2] and LeastMean Squares (LMS) [2]. The reviewer role uses TD-Learning in order to learnthe user preferences in the subjects he/she likes to review. The chair role usesLMS to learn the reviewer preferences. In order to gather information relevantto the learning process, user agents supervise the executions of their ownactions, the feedback from the users, their interactions with environmentcomponents and their collaborations. Figure 2 presents the learning-relatedcomponents in addition to the basic agent design shown in Figure 1.The combination of the Observer pattern [5] with the Strategy pattern [5] is aflexible approach to the OO design of the learning concerns [6, 7]. TheObserver pattern implements the mechanism for event monitoring andinformation gathering, while the Strategy pattern makes it flexible with respectto the learning strategies. Consider a concrete example of this approach in thecontext of the EC system, as shown in Figure 2. In such a system, the goal ofthe Observer pattern is to notify the learning components of relevant eventsthat trigger the learning process. Operations on Plan, Agent, Role classes aremonitored to provide the learning component with contextual information andstart the learning process. The Agent and Role classes do not directlyimplement the Observable interface because some agents and roles have not thelearning property. The LearningComponent class implements the Strategy patternand represents a family of different algorithms that implement the learningtechniques. The TD-Learning and LMS subclasses implement the specificlearning algorithms.Figure 2. Learning: the Observer Pattern with the Strategy Pattern. However, the OO design of the learning concerns has a huge impact on the agent structure. Learning issues crosscut multiple class hierarchies representing other agent concerns, such as collaboration and the agent’s basic functionality. As shown in Figure 2, although part of the learning concerns is localized in the classes of the Strategy pattern, learning-specific code replicates and spreads across several class hierarchies of a software agent. Several participants (e.g. Chair, Reviewer, UserAgent, and Plan subclasses) have to implement the observation mechanism and consequently have learning code in them. Some classes (e.g. the RevisionProposal class) have learning-specific knowledge. Adding or removing the learning code from classes requires invasive changes in those classes.Note that even if we try to refactor the OO solution presented in Figure 2, we cannot find a more modular solution. One alternative solution is to try to move the learning-specific methods and attributes from the agent classes to a new class. However, the following problems still remain: (i) the agent classes need to keep an attribute with a reference to this new learning-related class, and (ii) the code relative to the information gathering remains scattered over the methods on other agent classes (for example, the method judgeProposal() in Figure 2). These problems happen because learning is acrosscutting concern independently of the object-oriented decomposition used.Problem Object-oriented abstractions do not support the modularization of the learning concerns. The design of the learning issues tends to affect or crosscut manyclasses and methods that implement other agent concerns. This makes it hardto distinguish between the learning protocol and other agent concernsinvolved [9, 11, 20, 21]. Adding, removing or modifying the learning concernsto/from a system is often an invasive, difficult to reverse change. How do weseparate the learning-specific concerns from the other concerns? The followingforces emerge from this problem:•Transparency. The design solution should support the introduction oflearning behavior into existing systems in a way that is transparent to therest of the system.•Reusability. The basic learning protocol should be easy to reuse to differentagent types and roles.•Readability and Maintainability. Agent classes, which modularize the agent’sbasic functionality, should not be polluted with learning-specific knowledge.Moreover agent classes should not be mixed with invocations of learning-specific methods in order to improve the system readability andmaintainability.•Ease of Evolution. The design of the learning concerns should be easy toevolve as new learning-related requirements need to be satisfied. Changeson the definition of observed events and on the learning strategies shouldnot affect the basic agent functionality.•Code Replication. The design solution should minimize code replicationacross different classes and methods of the multi-agent system.•Flexibility. The design should be flexible enough to support the associationof different learning strategies with distinct agent types and role classes.•Generality. The solution should be general enough to support themodularization of the learning concerns independent of the used machinelearning techniques.Solution Use aspects1 to improve the separation of the learning concerns (Figure 3).Learning aspects are used to modularize the entire learning protocol,including the learning-specific knowledge and the information gathering. TheLearning aspect separates the learning protocol from agent classes, such asagent types, plans, and roles. By using Learning aspects, we define when andhow the agent learns. They specify how to extract information from diverseagent components which are necessary to enable the agent learning.The Learning aspects connect the agent classes with the correspondinglearning components, making it transparent to the agent’s basic functionalitythe particularities of the learning algorithms in use. These aspects are able tocrosscut join points1 in the agent classes in order to change their normalexecution and invoke the learning components. The join points include thechange of a knowledge element, execution of actions on plans, roles, andagent types, or still some threw exception. Auxiliary classes are used toimplement different learning techniques. Agent classes and Learning aspectsare combined through a weaving process, as illustrated in Figure 3.1 Appendix A presents a brief overview of terminology related to aspect-oriented design.Figure 3. Diagram for Learning Aspect using the “Dog Learning” Example Structure Figure 4 illustrates the structure of the Learning Aspect pattern. The design notation is based on an aspect-oriented modeling language [16, 17], which isused throughout this paper. This language extends UML with notations forrepresenting aspects. The notations provide a detailed description of theaspect elements. In this modeling language, an aspect is represented by adiamond; it is composed of internal structure and crosscutting interfaces.The internal structure declares the internal attributes and methods. Acrosscutting interface specifies when and how the aspect affects one or moreclasses [16, 17]. Each crosscutting interface is presented using the rectanglesymbol with compartments (Figure 4). A crosscutting interface is composed ofinter-type declarations, pointcuts and advices. The first compartment of acrosscutting interface represents inter-type declarations, and the secondcompartment represents pointcuts and their attached advices. The notationuses a dashed arrow to represent the crosscutting relationship, which relatesone aspect to classes and/or aspects.The Learning Aspect pattern has four participants:•Learning Aspect-defines the general learning protocol.•Specific Learning Subaspect-implements the part of the learning that is specific to an agent typeor role.•Learning Component-implements a specific learning technique.•Agent Element-provides relevant events and contextual information for learningpurposes – this element can be a plan, an agent, a role, or other classesthat are part of the agent. They do not have any learning-specific code.Figure 4. The Static View of the Learning Aspect Pattern.In the structure of the Learning pattern (Figure 4), some parts are common to all instantiations of the pattern, and other parts are specific to each instantiation. The common parts are:1. The general learning protocol (Learning Aspect):a. learning components are initialized,b. events are sensed,c. contextual information is gathered,d. learning components are called, ande. the agent knowledge is adapted.2. The list of Learning Components in the Learning Aspect, i.e. thereferences to components that implement more sophisticated learning strategies.3. The learning-specific knowledge.4. The general structure of the Learning Components.The specific parts are:5. The definition of the specific events associated with an agent type orrole.6. The specific information gathering.7. The initialization of specific learning components used.8. The adaptation of the agent knowledge.9. The implementation of the specific Learning Components.The purpose of the Learning aspect is to make the agents able to learn. The Learning aspect extends the agent classes to introduce the learning protocol to them. The Learning aspect has three main parts: the aspect itself and twocrosscutting interfaces. The aspect holds the list of specialized learningcomponents, and the methods to update the agent knowledge since newconclusions are obtained from the learning components. The crosscuttinginterfaces define how the Learning aspect crosscut different classes of thesoftware agents.The InformationGathering interface defines the join points that describethe relevant events and the information which must be gathered from theagent/role classes in order to enable the learning process. This interfacecontains the advices which invoke either methods responsible forimplementing a learning behavior or a specific learning component. Theadvices usually run after executions of methods on agent classes, roleclasses and plan classes, and other classes eventually associated with theagent. The LearningKnowledge interface introduces different learning-specific attributes and methods into different agent/role classes based oninter-type declarations.Note that all the learning code is removed from the agent classes and isseparately implemented in associated learning aspects, as explained above.The learning code consists of learning aspects and auxiliary classes devotedto implement specific learning strategies. When the learning aspects arewoven with the system code, they essentially affect several agent classes; theweaving process is required to compose the learning design with the otheragent concerns, such as the agent’s basic functionality and roles.Dynamics Figure 5 presents the basic pattern dynamics: (i) the Learning Aspect detects that a relevant operation (join point) on an agent/role class was performed,(ii) the Learning Aspect intercepts this operation, (iii) the Learning Aspectgathers event-related information through the advice parameters, (iv) theLearning Aspect optionally updates some learning-specific knowledge, (v) theLearning Aspect selects and calls the corresponding Learning Components,providing them with the event-related information, (vi) the LearningComponents process the new information, (vii) if they get a conclusion, theLearning Aspect updates the attributes of the agent/role classes.Figure 5. Dynamic View of the Learning Aspect PatternSeveral events can trigger the agent learning [1, 2, 3, 4], including the execution of internal agent actions, throwing of exceptions, messages exchanged between agents, and events sensed in the external environment. The pattern dynamics is illustrated in the next section in terms of the example.Solved Example Figure 6 illustrates the pattern instantiation for the EC system. The Learning aspect and its subaspects crosscut about 12 different classes in this system. However, the figure only presents a partial set of the classes affected by the learning aspects; it shows the Reviewer class, the RevisionProposal class, the UserAgent class, and the JudgementPlan class. The Learning aspect has two subaspects: ChairLearning and ReviewerLearning; Figure 6 illustrates only the ReviewerLearning subaspect.Figure 6. The Learning Pattern for the Reviewer Role.The ReviewerLearning aspect affects the action of judging a proposal in order to learn the user preferences. The execution of the judgeProposal() method on the JudgementPlan class is an important event for the learning purpose; once the judgment is concluded, the judgement-related information is used by the learning aspect in order to learn about the user preferences. The ReviewerLearning aspect catches the information associated with the proposal judgement and the associated learning component is invoked (the TDLearning class in this case).The ReviewerLearning aspect also intercepts methods on the Reviewer class, and on the UserAgent class. Figure 6 also illustrates how the LearningKnowledge interface of the Learning aspect modifies the structure of the RevisionProposal class. This interface introduces the attributes paperInterest and evaluation and the associated “setters” and “getters” so that the chair role can learn based on the reviewer evaluation.Figure 7, presents the pattern behavior when the ReviewerLearning aspect detects that an important action on an agent plan was performed and learning is required:•The judgement plan is executed.•Judgement actions are performed by calling the method judgeProposal().•The ReviewerLearning aspect detects the judgement result by intercepting the end of the method execution.•This aspect gathers the information needed from the plan context, i.e. the RevisionProposal object.•The aspect updates the RevisionProposal object so that the chair can learn based on the reviewer judgement – it updates this object state by invoking the methods setPaperInterest() and setEvaluation(), both of them introduced by the Learning aspect.•The ReviewerLearning aspect selects and calls the corresponding learning components, the TDLearning class in this case, and provides them with the contextual information.•The aspect executes its specific algorithms and alternatively gets a conclusion which leads to the adaptation of the agent knowledge, in this example the update of the user’s research interests in the UserAgentFigure 7. Learning the Reviewer Preferences.Consequences The Learning Aspect pattern has the following consequences:•Transparency. Aspects are used to introduce the learning behavior into agent classes in a transparent way. The description of which agentclasses need to be affected is present in the aspect and these monitoredagent classes are not intrusively modified.•Improved Separation of Concerns. The learning protocol is entirely separated from the other agent concerns, such as the agent’s basicconcerns and interaction. The classes and aspects associated with otheragent concerns have no learning code.•Reusability. The basic learning protocol is modularized in a generic learning aspect, which can be reused and refined to different contexts.•Readability and Maintainability. The agent kernel is not intermingled with invocations of methods responsible for the learning implementation. As aconsequence, the pattern solution improves readability, which in turnimproves maintainability.•Ease of Evolution. As the multi-agent system evolves, new agent classes may have to be monitored and trigger the learning process. Agentdevelopers need only to add new pointcuts in the learning aspects inorder to implement the new required functionality.•Reduced Code Replication. The pattern supports the isolation of the learning protocol in learning aspects, minimizing the code replication.•Flexibility. The pattern solution is flexible enough to support the association of different learning strategies with distinct agent types androle classes.•Generality. The solution of the Learning Aspect pattern is general enough to support the modularization of the learning concerns independent ofmachine learning techniques in use. The pattern solution presents thecentral components required in the learning techniques.Although the learning-specific concerns are completely defined apart fromother agent concerns, the use of the pattern imposes some problems to theagent designer:•Required Refactoring. In some circumstances, the realization of the Learning Aspect pattern requires restructuring of the base codeassociated with other agent components in order to expose suitable joinpoints. In this way, capturing the learning concerns as aspects sometimesrequires restructuring of the classes and methods to expose suitable joinpoints. For instance, we have extracted code from existing methods of aplan class into a new method to expose a method-level join point so thatthe learning aspects can intercept it. Tools to help in the refactoringwould make it easier to introduce aspects into an existing system.•Description of Learning Aspects Depends on Specific Core Classes. The names of agent classes, role classes and plan classes appear in thedefinition of pointcuts in the learning aspects. The description of aLearning Aspect cannot be directly applied to other agents.•Introduction of More Design Elements. The Learning Aspect pattern introduces new design elements (aspects) to promote the separation of thelearning concerns. This solution introduces another level of indirection.Variants Reflective Learning.This variant is similar to the aspect-oriented solution presented here. However, this variant rests on the use of the Reflectionarchitectural pattern [29]. This reflective solution uses learning meta-objectsas an alternative to learning aspects. Each learning aspect is a meta-classand learning subaspects are defined subclassing this meta-class. TheLearningKnowledge crosscutting interface is defined as attributes internalto the learning meta-classes. The InformationGathering crosscuttinginterface is defined using the meta-object protocol that intercepts themethods calls (events) to objects and redirects the control flow to meta-objects. The disadvantage of this reflective variant is that it requires a meta-object protocol which usually introduces changes to the virtual machine. Inaddition, reflective solutions do not directly support the composition of thelearning meta-classes with other meta-classes modularizing othercrosscutting concerns. As the agents’ complexity increases, goodcomposition mechanisms are essential to the system reusability andmaintainability.Known Uses Developers have been using a design solution similar to the Learning Aspect pattern to implement the Brainstorm framework for multi-agent systems[20]. This framework implements the reflective learning variant. TheLearningAspect elements are implemented as meta-objects. We have alsoimplemented the Learning Aspect pattern both in the EC system [21] and inthe Portalware system [10, 11]. The Portalware system has learning aspectsassociated with information agents in order to optimize user queries. Thequeries are intercepted by the aspects, which is the information used bylearning components to build the user profiles. The user profiles are used tooptimize the next user queries.We know other software projects that implement learning in an OO mannerand could use this pattern. Some of these systems are the following:• A real system [7, 13] developed for the participation in the Trading AgentCompetition (TAC) [30]. TAC is an international forum designed toencourage high quality research on competitive trading agents. The multi-agent system in TAC operates in a shopping scenario of goods fortraveling purposes. The artificial agents are travel agents that buy andsell airplane tickets, hotel rooms, and entertainment tickets for clients.There are two types of intelligent agents in this system whichincorporates machine learning techniques: the Hotel Negotiator Agent andthe Price Predictor Agent. The former uses: (i) a minimax decision tree [3]and an evaluation function based on perceptrons [2] (neural networks) tomodel the agent knowledge, (ii) a Learning aspect to modularize theauction history and the final results of the auctions (learning-specificknowledge), and the specification of methods called to finalize theauctions (information gathering) - the events that trigger the agentlearning, and (iii) a Learning component that implements the TD-Learningalgorithm. The second agent uses: (i) an exponential smoothing technique[34] to model the agent knowledge, (ii) a Learning aspect to separate theask prices and last predicted ask price (learning-specific knowledge), andthe specification of auction-related methods that are called in eachminute of the game (information gathering), and (iii) a Learning componentwhich implements the Back Propagation [2, 4] and LMS algorithms.• A system [14] that implements the Tic-Tac-Toe game. The agents here usea minimax decision tree [3] and neural networks to implement the agentknowledge. A Learning aspect encapsulates the player trajectories andthe final result of the game (learning-specific knowledge), and thespecification of methods called to make new plays and to finalize thegame (information gathering). A Learning component was used toimplement an algorithm for adaptive dynamic propagation [3].See Also The Learning Aspect pattern is a variant of the Learning pattern [19]. The Learning Aspect pattern is alternatively related to the Role Object pattern[20] when this pattern solution is used to structure the agent roles; thelearning aspects learn based on the execution of role methods. TheLearning Aspect pattern contains the aspect-oriented implementation ofthe Observer pattern [22, 32]. The Strategy pattern [5] can be used toimplement different learning strategies. Finally, the implementation of theLearning Aspect pattern (see below) uses some idioms [23] for the AspectJlanguage [24], like Template Advice, Composite Pointcut, and AdviceMethod.Implementation We describe below some guidelines for implementing the Learning Aspect pattern. We give AspectJ [24] code fragments to illustrate a possibleimplementation of the pattern, describing details of the EC example.Although we illustrate an implementation of the Learning Aspect pattern inAspectJ, the pattern can be specified using a different aspect-orientedprogramming language following the guidelines presented.Step 1: How to define a Learning Aspect?A Learning Aspect must define the general learning protocol. This aspectmust define the attributes and methods common to all the learning aspectsin the system. For example, it holds a reference to the associated learningcomponents, an abstract method to initialize these components, and anabstract method to invoke the learning components.¬The EC system contains the implementation of a general Learningaspect to both chair and reviewer agent roles. This aspect is declared asabstract. Note that the initialization method is called by an after advice,which is in turn associated with an abstract pointcut. Pointcuts are usedto define which join points on the object execution the aspect is interestedto observe.These pointcuts must expose as parameters the information (objectinstances) necessary to be used in the aspect context. Advices associatedwith these pointcuts invoke methods on aspects and classes, and if it isnecessary they pass the information gathered in the pointcuts asarguments. The learningInstantiation pointcut describes when a specificlearning aspect should be initialized; it is abstract because it depends onthe agent type or role class associated with the specific learning aspect.This aspect also specifies the methods: (i) learnPreferences()– which isresponsible for invoking the learning components; and (ii)updatePreferences()– which updates the user research interests, after theexecution of the learning algorithm.public abstract aspect Learning {...protected Hashtable Role.learningComponents = new Hashtable();protected void abstract init(Role role);protected abstract pointcut learningInstantiation(Role role);after(Role role): learninInstantiation(role) {System.out.println("<* Learning *> initialization:" + ((Role)role).getName());init(role);}public Hashtable abstract learnPreferences(Hashtable currentInterests,Vector my_keywords, boolean newDecision, int currentPaperInterestDegree);public void updatePreferences(Hashtable currentInterests, Hashtable newPreferences) { ...}...}Step 2: Why the Learning aspect must be singleton?In general, each agent instance must have its own Learning aspect. As aconsequence, Learning aspects must be instantiated per Agent instance.The current version of AspectJ supports the specification of per-objectaspects. We could describe the instantiation of the Learning aspect usingperthis:public abstract aspect Learning perthis(Agent) {…}However, the use of perthis restricts the scope of the aspect. When oneAspectJ aspect is declared to be singleton or static, its scope is the wholesystem and the aspect can crosscut all system classes. Per-object aspectscan only crosscut the object with which it is associated. Since the learningprotocol crosscuts several classes, not only the Agent class or the Roleclass, the perthis clause cannot be used in this context. As a result, youhave to declare Learning aspects as singletons and introduce the methodsand attributes to the Agent and Role classes. This was the strategy followedin the definition of the learningComponents attribute described in Step1.。

基于多智能体元强化学习的车联网协同服务缓存和计算卸载

基于多智能体元强化学习的车联网协同服务缓存和计算卸载
第 42 卷第 6 期 2021 年 6 月
通信学报
Journal on Communications
Vol.42 No.6 June 2021
基于多智能体元强化学习的车联网协同服务缓存和计算卸载
宁兆龙 1,2,张凯源 2,王小洁 1,郭磊 1
(1. 重庆邮电大学通信与信息工程学院,重庆 400065;2. 大连理工大学软件学院,辽宁 大连 116620)
第6期
宁兆龙等:基于多智能体元强化学习的车联网协同服务缓存和计算卸载
·119·
均等问题[4-5]。尤其是随着移动应用的多样性增强, 其所需的资源也具有很强的异质性,这导致资源利 用率低的问题日益凸显。
人工智能和机器学习技术的不断发展,以及 其在多个领域的成功应用,使其正成为解决移动 边缘计算瓶颈问题的关键技术[6-7]。和传统技术相 比,人工智能技术对于环境的动态变化拥有更强 大的感知能力。作为其重要分支,深度强化学习 在资源分配方面已经得到一定的应用,文献[8-12] 都表明基于强化学习的车联网资源分配解决方案 具有较好的准确性和稳健性。随着用户需求的动 态变化以及多方主体(设备节点、边缘节点和云 服务器)的参与,车联网系统需要一种效率高、 均衡性强的任务调度和资源分配方法。同时,由 于边缘节点的资源有限,需要轻量化、分布式的 机器学习技术与其进行适配,从而完成高效的学习 过程。
本文主要的研究工作如下。 1) 本文构建了多边合作的车联网服务模型,它 联合了任务缓存和边缘任务调度问题,在可用资源 约束的情况下,最小化系统时延。本文将车联网服 务问题建模成一个混合整数非线性规划问题,并证 明求解该问题需要非多项式的计算复杂度。 2) 本文提出了一种双层的多 RSU 协同缓存框 架求解上述问题,它采用多智能体元强化学习框架 为 RSU 缓存车辆应用提供所需服务。每一个 RSU 作为一个本地智能体计算其对应状态下的缓存决 策,云服务器作为元智能体,采用长短期记忆 (LSTM, long short-term memory)结构的神经网络 来平衡本地智能体的决策,并维护自己的状态信息 来进行更快的策略学习。 3) 在缓存策略确定的情况下,本文提出一种自 适应的 RSU 协同卸载算法,它采用拉格朗日乘子 法来求解最佳协同卸载策略。本文通过二分迭代搜 索的思想搜索最优拉格朗日乘子,从而调度系统中 每一个 RSU 的计算任务,实现系统中所有 RSU 的 工作量负载均衡。 4) 本文采用杭州交通流数据进行实验,结果表 明本文提出的算法具有良好的效能和实用性。与其 他 3 种基准算法相比,本文提出的算法能够获得更 低的系统时延,并且能在大规模任务流下拥有相对 稳定的表现。
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A Multi-Agent Based System to Enable Strategic andOperational Design CoordinationRobert Ian Whitfield, Graham Coates, Alex H.B. Duffy, Bill HillsAbstractThis paper presents two systems which individually focus on different aspects of design coordination,namely strategic and operational.The systems were developed in parallel and individually contain related models that represent specific frames from a Design Coordination Framework developed by Andreasen et al. [1].The focus of the strategic design management system is the management of design tasks, decisions,information,goals and rationale within the design process,whereas the focus of the operational design coordination system is the coordination of tasks and activities with respect to the near-optimal utilisation of available resources.A common interface exists which enables the two systems to be integrated and used as a single system with the aim of managing both strategic and operational design coordination.Hence,the objective of this work is to enable the design process to be conducted in a timely and appropriate manner.1IntroductionDesign coordination is a relatively new concept within the engineering design community, which is aimed at improving the performance of the engineering design process.One of the most prominent frameworks associated within design coordination is the Design Coordination Framework(DCF)-Andreasen et al.[1].The DCF presents a number of frames,each of which is aimed at representing different aspects of design coordination.The DCF also describes the management of the links between the frames.There currently exists no implementation of the DCF and consequently the concepts have not been validated.This research has identified that certain elements of the DCF can be divided into two distinct areas,namely strategic and operational design coordination.Strategic coordination may be viewed as the management of the control mechanisms that govern a design process.The frames within the DCF associated with strategic coordination have been identified as the Goal/Result Model and the Discipline/Technology Model.These models have been implemented within the Design Management System(DMS).Operational coordination can be thought of as performing tasks in a near optimal manner with respect to time,and the allocation and utilisation of resources.The Resource Model and the Activity/Plan Model of the DCF are viewed as being associated with operational design coordination.These models have been implemented within the Design Coordination System(DCS).The Task Model frame within the DCF has been identified as the interface between strategic and operational coordination,and is common within both systems. Figure1represents the DMS and DCS which includes the frames within the DCF mentioned and the respective agent architectures.The DMS and DCS are discussed within section2and3respectively.These sections also describe the DCF frames used and how they have been represented.Section4briefly discusses the implementation of the two systems, and section 5 concludes this paper.2Strategic Design CoordinationThe Design Management System (DMS)was produced to enable distributed design activities to be coordinated in a strategic manner.This was achieved through the management of constraints,decisions,tasks and goals such that design activities may be conducted by the right person and for the right reason -Andreasen et al.[1].The requirement for a framework to coordinate distributed design activity is becoming increasingly important as the design of large made-to-order products is being distributed globally in order to reduce costs,gain competitive advantage and utilise external expertise and resources.Within these globally distributed design teams,individual designers specialise within their domain producing solutions to a distinct part of the overall design problem using the tools and techniques with which they are familiar.These tools rarely facilitate concurrency,producing solutions within a particular discipline without using or sharing information from other disciplines or aspects of the product model,and seldom considering stages within the product’s life-cycle other than conceptual,embodiment or detail.Conventional management and maintenance of consistency throughout the product model can subsequently become difficult to achieve since there are many factors that need to be simultaneously considered whilst making a change to the product model.Factors such as the propagation of change,management of constraints,and consideration of requirements require management and coordination for the design process to be performed successfully.2.1DMS ArchitectureThe DMS was developed as an agent-oriented architecture to enable design activities to be distributed as well as providing a means of utilising existing legacy software.However the focus of this work was not the construction of the agent architecture,but the mechanisms that wouldenable an agent architecture to be coordinated within an engineering design environment.DMS Discipline Model ProcessBuilder GoalModel Bridging AgentInformationManager Task ManagerActivity Director SchedulingAgentResource MonitorResource Manager ResourceModel Activity/Plan Model Coordination Manager AgentsDesign Task ModelOperational Design CoordinationStrategic Design Coordination Interface Figure 1. Strategic and Operational Design Coordination SystemsCommunica-ton ProtocolThe control mechanisms within agent-oriented architectures were discussed by Jennings[2] and were considered within the development of the coordination mechanisms of the DMS.Three scenarios were proposed by Jennings:•unlimited resources for agents such that each agent has a complete representation of its own and all other agents goals, tasks etc.,•limited resources for agents such that each agent has a partial representation of its own and all other agents goals, tasks etc., and,•limited resources with one agent having a complete representation of the goals and tasks that need to be achieved and undertaken such that it may govern the action ofthe other agents.Jennings discussed the benefits and limitations of each scenario and concluded that the second scenario would be most suitable for distributed architectures due the removal of communication bottlenecks,the limited resource availability,and the graceful degradation in performance due to the loss of an agent.Malone[3]proposed that agent-based systems should;not try to solve complex problems by themselves,have a flexible boundary between themselves and humans,and not try to do things that humans could easily do.Malone further suggested that agent-based systems should provide mechanisms which would enable humans to see and modify the same information and reasoning processes that the agents are using.The management of control within the DMS was split into two stages following the discussions of Jennings and Malone.The first stage provides mechanisms that enables the designer to represent the design process within a centralised framework.The process produced directly represents the activities,reasoning and goals that will be performed,undertaken and achieved by the agents.It also allows the process to be constructed manually rather than by providing complex mechanisms that allow the agents to autonomously determine the structure of the problem.The process may then be enacted in a distributed manner,using the centralised control framework to ensure that the process is capable of satisfying the required goals in a timely and appropriate manner.The second stage involves the communication of the centralised control knowledge to the agents,such that the control,as well as the activities are distributed.Managing the coordination in this manner:•removes the necessity of providing complicated coordination mechanisms to enable the agents to determine between themselves where their activities fit intothe process,•reduces the time taken to produce a representation of the design process,•provides a representation of the process that is easily understandable by humans, and,•enables the future distribution of the control knowledge removing communication bottlenecks and provides graceful degradation in the event of an agent failure.A number of frames were identified from the Design Coordination Framework developed by Andreasen et al.[1]as being useful within the development of a strategic design coordination methodology.These frames were the discipline/technology model,the task model and the goal/ result model.2.2The Discipline/Technology ModelWithin the context of the DMS agent architecture,the discipline/technology model contains information supplied from the design agents regarding a description of the tasks that they arecapable of undertaking.When a design agent becomes available,the agent describes the design activities that it is capable of performing to the DMS.A formalism of a design task was developed which could be applied to any level of abstraction and enabled the generic application of the discipline/technology model.It also allowed the agents to describe their capabilities in terms of low level tasks such as;calculate the stress of a component,or as higher level tasks,such as;design a component that meets the stress requirements.The task formalism was described as having the following characteristics:•prerequisites that must be satisfied before the task may be undertaken,•arguments that the task may operate upon,• a description of the task,•the associated discipline, and,•outcomes resulting from the enactment of the task.Using this formalism,it is possible to represent the tasks and design activities in terms of the associated disciplines,and when represented within the task model,to enable the interactions between disciplines as well as between tasks to be determined.2.3The Design Process Builder and Task ModelA graphical user interface was developed which would enable the designer to define the design process at any level of abstraction using information obtained from the discipline/ technology model.A number of different events were defined such as;perform file operation, perform task,branching operations(to facilitate concurrency),and decision events,which could be used to define the process as well as coordinate the activities of the design agents.These events as well as the connections and dependencies between the events are represented within a design structure matrix.Design structure matrices were used to represent the task model,and were extended to support the management of decisions as well as iteration loops.2.4The Goal/Result ModelThe goal/result model has not yet been implemented within the DMS but is intended to provide a representation of the specifications defining the requirements of the design solution. These specifications are broken down such that they may be related to particular parts of the design process.Each task within the design process has been formalised as having an outcome or result. Combining tasks within a process may be regarded as representing the activity that needs to be undertaken to satisfy a goal.These goals will subsequently be used to determine the appropriate design activity for meeting the specifications,as well as providing a measurement of how well the design solution satisfies the specification.3Operational Design CoordinationOperational coordination can be viewed as comprising of five fundamental components: activity,agent,order,location,and time.To satisfy a particular requirement an activity,or activities,need to be performed so that the associated task can be completed.Activities need to be specified such that when they are performed the associated task will be completed.An agent carries out the required activities to complete the task and may be considered as a either a human,software or hardware resource.The correct choice of agent,or agents,will ensure that the activity is performed in the most suitable fashion and the task is completed satisfactorily.Since relationshipscan exist between tasks,there may be an optimal order in which activities should be performed to complete the tasks.Consideration to this fact will assist in identifying those activities that can be performed concurrently and those that must be carried out sequentially.When an agent is performing an activity it may be appropriate to do so in a certain location.This consideration may be of particular importance and relevance when agents are working in the same team,or related teams,to complete the same task,or related tasks.In addition,design may be undertaken in distributed locations. For any activity, timeliness is usually of paramount importance.The Design Coordination System(DCS)aims to optimise the scheduling and planning of the tasks involved within the design process with respect to the allocation and utilisation of available resources.The DCS operates in conjunction with the DMS by performing the scheduling and planning activities upon the tasks that have been determined by the DMS to achieve a particular goal.The DCS consists of a number of different types of agents,each fulfilling a particular role and performing several different tasks with reference to the planning and scheduling of the design process.•The Coordination Manager registers agents and provides an introduction service such that related agents can locate each other.•The Resource Manager is responsible for ensuring that at all times optimal utilisation is made of the available resources in the design environment.•The Scheduling Agent,on instruction from the Resource Manager,invokes an optimisation package to create a schedule.•Activity Directors act on this schedule by directing Task Managers to complete their tasks by performing the required activities.•Prior to executing their tasks,Task Managers request input from their related Information Manager.•Resource Monitors constantly review their associated resource and inform the Resource Manager of any change.3.1Coordination ManagerAll of the agents within the DCS framework initially register their services by sending a message to the Coordination rmation contained within this initial communication relates to the agent’s attributes,facilitates inter-agent communication and enables agents to work cooperatively.This feature of agents having the ability to communicate directly with any other agent allows efficient message passing,removes communication bottlenecks,and promotes coordination.Message passing is efficient as communication only occurs when necessary.The Coordination Manager facilitates the decentralisation of communication amongst agents. Consequently,message bottlenecks are avoided and communication can occur directly and concurrently between agents, rather than via some centralised agent.The Coordination Manager is also responsible for constructing an agent matrix,which aids the replacement of agents which may have failed.The agent replacement mechanism exists which enables any agent that becomes unavailable to be replaced such that negative impact on the effectiveness of the agent community is minimised.3.2Information ManagerResponsibilities of this agent include ensuring that inputs are coordinated before and after the associated activity is performed on them.That is,they are added to or removed from the rightresource at the right time.Other duties include ensuring that any information associated with the task to which it has been assigned are made available to the related Task Manager.After a Task Manager has performed its associated activity to complete its task on a particular input,and prior to preparing another input,the Information Manager coordinates the output from the previous task. That is,the output may be removed from one resource and placed on another as input in preparation for the next activity to be performed.This procedure needs to be carried out after every activity is performed to avoid delays on any of the resources.3.3Task ManagerA relationship exists between a Task Manager and an Information Manager if they are associated with the same task.A Task Manager’s responsibilities include requesting inputs from its related Information Manager and subsequently supervising or performing the activity to complete the task on the input of the assigned resource.Once a task has been completed by a Task Manager the related Information Manager coordinates the output.Inputs continue to be requested from the Information Manager by the related Task Manager until all have been dispensed and each activity has been performed on them,and hence all tasks have been completed.That is,the design process is complete.3.4Resource ManagerThe Resource Manager is responsible for managing the available resources in the form of a resource model as shown in Table2.The resource model contains a status flag Sj and an efficiency measure Ej,where j={1,2,3,...,m}and m is the number of resources within the design environment.Resource Status EfficiencyR1S1E1R2S2E2R3S3E3..................R m S m E mTable 1: Resource ModelA status flag is an indication of the availability of a resource,such that S j={0,1}∀j.The efficiency is a relative measure of the speed of a resource,such that0≤Εj≤1∀j.The Resource Manager updates the resource model following the notification of a change in a particular resource’s efficiency by the associated Resource Monitor.The Resource Manager may then instruct the Scheduler to produce a new schedule following the change in efficiency of a resource below a threshold value.Similarly,the Resource Manager may also consider requesting a new schedule if a resource’s efficiency increases above a threshold causing it to be more efficient than a resource currently being utilised.The decision making process concerning whether or not to re-schedule,involves the Resource Manager taking into account several factors.The number of inputs remaining to be considered and the likelihood that a new schedule will be adhered to for the remainder of the design process should also be taken into account.3.5SchedulerThe Scheduler uses a Multiple Criteria Genetic Algorithm(MCGA)to facilitate the optimum utilisation of the available resources.The Scheduler views the scheduling problem as the minimisation of the total design time of a given number of tasks,with interdependencies between them, by assigning them to be performed on an optimum number of the most efficient resources. The Scheduler prepares the information required for the MCGA using information held in the resource model,and the task model,which is supplied by the DMS.When instructed by the Resource Manager,the Scheduler executes the MCGA to produce a Pareto optimal set of schedules.A prescribed criteria is then used to select the most appropriate schedule to enable the optimum utilisation of the available resources.3.6Resource MonitorA Resource Monitor exists which continuously monitors and records the efficiency and status of its associated resource.If a Resource Monitor observes a change in the status or efficiency of its associated resource,it will inform the Resource Manager providing the latest statistics.This may result in the Resource Manager deciding to either add or remove that particular resource from the design environment and request that a new schedule be produced.3.7Activity DirectorAn Activity Director is responsible for ensuring that the appropriate activities taking place on its associated resource are carried out in the correct order at the right time by the right Task Manager.In order to achieve this,each Activity Director constructs an Activity/Plan model based upon information provided by the Scheduler.Once the Task Manager receives this instruction it proceeds to perform the activity on a given input.On completion,the Task Manager informs the Activity Director that it has finished.The Activity Director then proceeds to instruct the next Task Manager in the local schedule to perform its activity on a particular input, and so on.4ImplementationDesign work commences with the design agents registering with the Design Management System(DMS).Agent details are registered within the discipline/technology model of the DMS. This initial communication will inform the DMS that the specified agent is available to undertake some design activity.The DMS requests that the agent model provides information regarding the nature of the design activity that it can undertake.This information will take the form of a list of tasks,details of files or parameters that the task may require,constraints that need to be satisfied prior to task enactment,and files and criterion that result from the enactment of the task.These task details may be either low-level or high-level terms and may describe an individual atomic task or a group of tasks depending upon the level of concern of the design engineer using the system.The designer would use the DMS to design the design process by decomposing it in terms of the relevant tasks available.Once the designer requires some particular design activity to be undertaken,the appropriate process is selected and the task model generated,describing all of the tasks that need to be undertaken,the dependencies between the tasks as well as a list of design concepts that need to be explored.Process selection is currently undertaken manually,however,the completion of the Goal/ Result Model will enable the determination of the appropriate processes to satisfy a particularrequirement.The task model is subsequently transferred to the Design Coordination System (DCS).Upon receipt of the task model,the DCS produces a near optimal schedule for the tasks to be completed.Depending on the behaviour of the resources within the design environment,one or more near optimal schedules may be created and implemented throughout the period of the design process.Once a schedule is constructed it is divided into the appropriate number of activity/plan models.Task enactment is then directed by the agents within the DCS such that the design agents concerned can proceed in completing the tasks.5ConclusionsThe Design Management System and Design Coordination System are complimentary CAD packages,which aim to encapsulate characteristics of coordination and implement them such that the design process can be performed efficiently.Indeed,the primary objective of the combined effort of these two systems is to enable design to be conducted in a managed and controlled fashion at both a strategic and operational ing a case study related to the design of turbine blades, early indications from the use of these two systems in conjunction with each other are that the design process can be coordinated at both a strategic and operational level with the outcome of a more efficient performance of the associated design process.AcknowledgementsThe authors gratefully acknowledge the support given by the Engineering and Physical Science Research Council who provided the grant RES/4741/0929that enabled this work to be undertaken.References1.Andreasen,M.M.,Duffy,A.H.B.,MacCallum,K.J.,Bowen,J.,&Storm,T.,“The Design Co-ordination Framework:key elements for effective product development”,International engineering design debate:The design productivity debate,Meeting;1st Sept.1996, Glasgow, pp. 151-174.2.Jennings,N.R.,“Coordination Techniques for Distributed Artificial Intelligence”,Foundations of Distributed Artificial Intelligence(eds.G.M.P.O’Hare and N.R.Jennings), Wiley, 1996, pp. 187-210.3.Malone,T.W.,Lai,K.,&Grant,K.R.,“Agent for Information Sharing and Coordination:AHistory and Some Reflections”, Software Agents, AAAI Press, 1997, pp. 109-143.Dr. R.I. Whitfield, Mr. G. Coates, Prof. B. Hills,Dr. A.H.B. DuffyNewcastle Engineering Design Centre,CAD Centre,Armstrong Building,James Weir Building,University of Newcastle upon Tyne,Montrose Street,Tyne and Wear, NE1 7RU, England.University of Strathclyde,Tel.: +44 (0)191 222 8556Glasgow, G1 1XJ, Scotland.Fax: +44 (0)191 261 6059Tel.: +44 (0)141 548 3005E-mail: r.i.whitfield@ Fax: +44 (0)141 552 3148E-mail: alex@。

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