Multi-agent Systems for Flexible Manufacturing Systems Management”. In From Theory to Prac

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多Agent系统理论概述

多Agent系统理论概述

多Agent系统理论概述摘要:Agent在AI(AI:Artificial Intelligence)研究领域已经成为热点,Agent 技术提供了一种新的计算和问题求解规范。

本文简要的讨论Agent、多Agent系统。

关键词:多Agent系统概述1Agent概述1.1Agent的基本概念Agent的概念最早出现在20世纪70年代的人工智能中,80年代后期,被译为“代”理,“智能体”或“智能主体”。

这些概念在许多领域被引用,不同的研究领域和内容,给出了许多不尽相同的定义。

目前为止还没有一个对Agent统一的定义,但多数研究者接受wooldridge和Jelinings所提出的Agent定义,即Agent 是一个具有自治性、社会能力和反应特性的计算机软、硬件系统,它具有自治性、社会能力、反应性和主动性。

1.2Agent具有的特性根据wooldridge的定义,对于Agent所应具有以下特征:1.自治性(Autonomy):Agent一般都具有自己的资源和局部于自身的控制机制,能够在没有外界直接操控下,根据自身的内部状态以及感知的外部环境信息,决定和控制自身的行为。

2.社会能力(Social Ability):Agent之间并不是孤立的。

和人一样,Agent具有通信能力,能够通过某种Agent通信语言与其他Agent进行各种各样的交互,也能和其他各类Agent一起有效地完成各种层次的协同工作。

3.反应性(Reactivity):Agent能够及时地感知其所在外部环境的变化,并能够针对一些特定的时间做出相应的反应。

4.主动性(activity):Agent能够遵循其承诺采取主动行动,表现出面向目标的行为。

它要求Agent保持比较稳定的目标,它的动作都是以此目标为依据的,从而产生一种叫做目标指引的行为(Goal Directed Behavior)。

1.3Agent分类从不同的角度,Agent有下面几种分类方法:1.根据Agent的存在形式:分为有形Agent和无形Agent。

基于多智能体系统的机器学习技术

基于多智能体系统的机器学习技术

基于多智能体系统的机器学习技术一、引言人工智能技术的发展已经逐渐在各个领域内发挥越来越重要的作用。

在其中一个重要的领域,机器学习技术已成为了近年来最为热门的话题。

不仅如此,基于多智能体系统的机器学习技术更是受到了广泛关注。

本文将为大家介绍这一技术的相关概念、应用以及发展状况。

二、多智能体系统和机器学习的基础概念多智能体系统(Multi-Agent System,MAS)指的是多个智能体之间通过有限的通信和协作,完成共同的任务和目标的系统。

在MAS中,智能体可以是任何有自主行为和能力的实体,它们可以是虚拟实体或者是物理实体。

而机器学习(Machine Learning,ML)则是一种无需明确编程指令的人工智能技术,它借助大量数据和统计方法,让机器通过学习自我进化,从而使得机器通过不断地提高自身的能力来完成各种任务。

三、多智能体系统中的机器学习技术在多智能体系统中,机器学习技术广泛应用于智能体之间的协作与交互过程中,以提高智能体之间的交互效率、信息共享和任务完成能力。

在这种情况下,机器学习技术主要包括以下几个方面的应用:1. 基于强化学习的多智能体系统强化学习(Reinforcement Learning,RL)是一种机器学习算法,它通过智能体在环境中的行为和反馈,让机器在最小化损失的同时,逐步提高自身的能力。

在多智能体系统中,基于强化学习的多智能体系统可以在智能体之间建立合作和竞争的关系,从而实现任务的完成和目标的达成。

2. 基于深度学习的多智能体系统深度学习(Deep Learning,DL)是一种机器学习算法,它所依托的神经网络模型可以对海量数据进行高效处理,从而实现图像、语音、自然语言等领域中的识别和分类问题。

在多智能体系统中,基于深度学习的多智能体系统可以实现复杂任务的共同完成,例如图像辨识、自然语言处理等。

3. 基于一阶逻辑的多智能体系统一阶逻辑(First-Order Logic,FOL)是一种基于谓词逻辑的形式化语言,它广泛应用于计算机科学、哲学等领域。

基于Multi-Agent的国民经济动员系统建模与仿真研究共3篇

基于Multi-Agent的国民经济动员系统建模与仿真研究共3篇

基于Multi-Agent的国民经济动员系统建模与仿真研究共3篇基于Multi-Agent的国民经济动员系统建模与仿真研究1随着社会和科技的发展,国家经济发展和管理也变得越来越重要。

国民经济动员系统是国家在经济领域进行有效调控的关键所在,它的建立和完善对国家的发展至关重要。

本文将介绍基于Multi-Agent的国民经济动员系统建模与仿真研究,以探讨系统的有效性。

首先,我们需要了解什么是Multi-Agent系统。

Multi-Agent系统是一种由多个智能体组成的系统,这些智能体可以协同工作和相互影响,以实现特定目标。

在国民经济动员系统中,Multi-Agent系统可以模拟多个经济参与者的行为,例如政府、企业、消费者等,并利用这些模拟结果进行决策和规划。

Multi-Agent系统的建模需要考虑到系统中智能体的性质和行为。

每个智能体都有自己的特定属性和行为,如经济利益、生产能力、消费需求等。

同时,智能体们可以相互交互和影响,例如政策制定、供需关系等。

因此,在建模时需要考虑到多个方面的因素,以准确地反映真实的经济环境。

在建模完成后,我们需要通过仿真研究来验证系统的有效性。

仿真可以通过运行多次模拟,分析不同经济事件下系统的表现和结果,并比较与实际情况的差异。

通过分析不同情景下系统的表现和分析结果,我们可以调整和优化系统的结构和规则,以达到更好的调控效果。

基于Multi-Agent的国民经济动员系统建模与仿真可以帮助国家实现更加有效的经济调控和管理。

它可以准确地模拟真实的经济环境,指导政策制定和决策;它可以测试各种方案的可行性,以减少失败率和风险;它可以指导对经济规律的深入了解,以更好地促进国家的稳定和发展。

综上所述,基于Multi-Agent的国民经济动员系统建模与仿真是一种有效的经济调控和管理工具。

通过模拟和分析,我们可以更好地理解经济环境和规律,在此基础上制定科学合理的政策和决策,以实现国家繁荣和发展基于Multi-Agent的国民经济动员系统建模与仿真是一种具有广泛应用前景的有效的经济调控和管理工具。

基于协商的多Agent供应链智能管理系统

基于协商的多Agent供应链智能管理系统

基于协商的多Agent供应链智能管理系统于丽娜【摘要】介绍一个多Agent系统(MAS)在供应链智能管理中的应用.提出框架并描述大量的谈判行为句,用于构建功能代理合作双方和第三方谈判的协议.还提供一个通过解决分布式约束满足问题来建立虚拟链的例子.%This paper describes a multi- agent system (MAS) which applies to the supply chain management. In the framework, functional agents can participate in, stay or leave the system. The function of Supply Chain Management Intelligent System (SCIMS) is implemented through agent -based negotiation. When the order arrives, a virtual supply chain will be established by automatic or semi -automated process of negotiation between different functional agents. This paper presents the framework and describes a lot of negotiation performatives, which can be used into building pair - wise and third - party negotiation protocol for functional agents. This article also provides an example of creating a virtual chain by solving a distributed constraint satisfaction problem.【期刊名称】《科技管理研究》【年(卷),期】2011(031)011【总页数】4页(P97-100)【关键词】谈判;多Agent系统;供应链管理系统【作者】于丽娜【作者单位】江西科技师范学院数学与计算科学学院,江西南昌330038【正文语种】中文【中图分类】F252电脑软件和硬件开发引起了智能软件代理的出现。

基于多Agent系统的自调节及协同工作的组合投资模型

基于多Agent系统的自调节及协同工作的组合投资模型

基于多Agent系统的自调节及协同工作的组合投资模型王雪松;申群太【摘要】Agent技术特别是多Agent系统(MAS,Multi-Agent System)为解决人工智能等领域复杂问题提供了一个新途径,多Agent系统重点研究如何协调系统中的各个Agent的行为使其协同工作.针对多阶段组合投资问题,提出了一个基于多Agent系统的自调节及协同工作的组合投资策略模型.该模型系统中的各个Agent 通过通讯共享知识,在求解问题的搜索空间中进行协同搜索,在更短的搜索步长内得到问题的解,极大地提高了系统性能.该模型具有不基于任何股票模型、时间复杂度低以及逼近最优投资策略速度较快等优点,实验证明具有一定的实际意义.【期刊名称】《计算机技术与发展》【年(卷),期】2010(020)005【总页数】4页(P117-120)【关键词】在线多阶段组合投资;多Agent系统;在线学习;协同搜索【作者】王雪松;申群太【作者单位】中山火炬职业技术学院,信息工程系,广东,中山,528436;中南大学,信息科学与工程学院,湖南,长沙,410083【正文语种】中文【中图分类】TP301.60 引言智能Agent是人工智能领域发展起来的具有感知能力、问题求解能力和外界进行通讯能力的一个实体[1]。

多Agent系统是分布式人工智能研究的一个前沿领域,它重点研究如何协调系统中的各个Agent的行为使其协同工作[2]。

构造结构复杂、知识丰富和功能强大的单Agent系统和由多个结构、功能较为简单的单Agent组合的多Agent系统[3]是Agent技术的两个主要发展方向。

多Agent系统是指一个为了完成某些任务或者达到某些目标,由多个Agent构成进行协同工作的计算机系统。

它可由众多同构的或者异构的Agent组成。

一个进行协同搜索的多Agent 系统中,Agent的特征包括多Agent系统的总体性能描述、单独的Agent的搜索算法、Agent的搜索结果是否需要合并及如何合并等。

基于Multi—Agent的武器装备虚拟维修训练系统

基于Multi—Agent的武器装备虚拟维修训练系统
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基于Multi-Agent理论的飞机故障协同诊断模型研究

基于Multi-Agent理论的飞机故障协同诊断模型研究

2017年第24卷第7期基于Multi-Agent 理论的飞机故障协同诊断模型研究P 陆江华1徐贵强2(1.成都航空职业技术学院航空工程学院,四川成都610100;2.成都航空有限公司技术工程办公室,四川成都610200)摘要:随着我国民航事业的迅速发展,如何保障飞机的飞行安全成了日益重要的问题[1]。

解决这一问题的关键就是及 时准确地对故障进行分析和诊断。

根据飞机远程故障诊断的实际需求及当前基于角色的协同诊断模型中存在的问题, 应用Multi-Agent 理论对民航飞机远程故障的协同诊断做了一些探索性研究。

关键词:飞机故障诊断;Multi-Agent 系统;协同机制;UML 协作图 doi :10.3969/j . issn . 1006 -8554.2017.07.0021基于Multi-Agent 的被动协同机制针对基于角色的飞机故障协同诊断模型存在的问题,将引人Multi-Agent 思想,定义参与诊断的实体的功能和结构,将其 封装为诊断Agent ,并对Agent 之间的协同机制以及Agent 与协 同环境之间的交互关系进行重点研究[2] 3。

1.1 诊断Agent 的功能在Multi-Agent 的协同诊断环境中,每个参与诊断的实体可 以抽象为一个诊断Agent 。

按照飞机故障诊断的实际需求,诊 断Agent 的功能如图1所示。

诊断Agent实时监控 知识获取 故障诊断 数据维护协同诊断11111故障提交过程监控决策提交决策评价图1诊断Agent 的功能1) 飞机运行状态数据的实时监控的功能:用户可以对飞机 运行状态数据进行实时观测,当出现异常数据时,诊断Agent 的实时预警机制会向用户发出提示。

2)飞机故障数据的特征信息获取:诊断Agent 对飞机故障数据提供了数据预处理功能,通过一系列模块操作,最终获取 飞机故障数据中的关键特征信息。

这是后续对故障信息进行 分析诊断的必要准备。

Multi-agent System中多维度信誉模型设计

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Mu l t i - d i me ns i o n Re p u t a t i o n Mo d e l De s i g n i n 程. 信誉 是 简化复 杂 系统 的有效 机制 , 可 以成 为解 决 Mu l t i — a g e n t S y s t e m Ag e n t 交互选 择 问题 的一种 简单 的方法 . Ag e n t 在交
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徐 莉, 余 红伟
( 武汉大学 经济与管理学院 , 湖北 武汉 4 3 0 0 7 2 )
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Multi-Agent System for Flexible ManufacturingSystems ManagementKrzysztof Cetnarowicz,Jarosław Ko´z lakInstitute of Computer ScienceAGH-University of Mining and MetallurgyAl.Mickiewicza30,30-059Krakow,Polande-mail:cetnar@.plAbstract.The paper focuses on the application of a multi-agent system for amanagement process.The presented system is working on the structure of graph,where nodes represent decision modules and edges–technological processes.Kinds of agents working in such environment are presented and an overview ofthe interaction protocols which can be used in such a system is given.1IntroductionThe development of production systems introduces new tasks,such as optimal produc-tion systems designing,efficient and safe production systems structure management or management of optimal functioning of a production process.On the other hand, proper management of a production system structure is very important for the enter-prise rentability and common problem.The contemporary production system becomes more and more complex and must beflexible for modifications.It makes a management process very difficult,inefficient and expensive.It seems that these tasks should be supported in their realization by computer sys-tems,or even totally managed by them.It may be realized when management systems are provided with models of a production process realized as decentralized systems. Attempts at realizing such systems improving the process of production systems func-tioning can be found in[7].An optimal movement of processed objects in the production system depends on a proper structure of the production system layout and real time routing,which can make use of the current production system structure in an optimal way.A proper choice of the production system layout structure has a basic influence on the effectiveness of activity. Consequently,it seems that multi-agent systems can be used as a model for a more complex computer management by means of effective object(parts)routing,but the evolution of the production system layout must be steered dynamically,thus adapting its structure to the current needs.2Principles of functioning of decentralized system managing computer networkWe may determine two goals of the computer management system for the production process[13],[1],[9],[5](fig.1):522nd International Workshop of Central and Eastern Europe on Multi-Agent Systems –management in real time to obtain the optimal functioning of the production pro-cess,–management of the production process structure(layout)to be adapted to the pro-duction realization.Fig.1.Schema of a computer system(with multiagent model)for production process manage-mentTo develop a multi-agent managing system for the production process we have to take a particular point of view to consider the production process as a graph environment for the agents population.In general,the production system may be represented as a network with boxes representing technological operations such as:milling machine, lathers,drills,grinder,etc.with a common operation:transport of partsfig.2.The parts enter the system,are treated and leave the system at the output.Fig.2.Schema of the production system layout consisting of milling machine,lathers,drills,heat treat,grinders,gear cutting and assembly as an output with circulating parts1,2,3,4.So the environment of that production system could be considered as a directed graph created in the following way(fig.3):–The production system is composed of a technological operation and edges repre-senting the operation,that follows.–The transport of parts between operations is considered as a technological opera-tion.–A node is placed between every technological operation.CEEMAS’01,26-29September2001,Krak´o w,Poland53–The whole production system layout may be mapped to a graph called a precedence diagram.–The edges represent technological operations,the nodes-decision nodes linking following operations and enables to take decision to switch the way of a part and select a proper path in the system.–The part is treated in the system following a properly chosen path in the graph.Fig.3.Schema of the production system layout-a,corresponding precedence diagram-b.The optimal treatment of a given part may be considered from two points of view:–point of view of the part:use the cheapest or the quickest way of operation execu-tion with a given quality standard,–point of view of the system:prepare the best adaptation of the system to perform demanded operations(be competitive)or use the cheapest way of the system mod-ification to adapt it to the treated parts needs(demand).The optimal management may be expressed in two ways:–Production management system functioning is based on the essential principle that defines conditions of a sending treated parts between the nodes following the opti-mal path(that is created in real time)of the part treatment.–The production system layout must be dynamically adapted to the treatment needs in an optimal way.These two points are dependent between them and must be considered as an in-tegrity during the management process.The main principle of the multiagent management system functioning is that the management process bases on the marked oriented decisions methods.To make a pos-sible application of the marked oriented management,it is necessary that every part pay for the technological operation executed on it.It means that a part pays for transfer542nd International Workshop of Central and Eastern Europe on Multi-Agent Systems through every edge between the nodes.Therefore a part is considered as a special agent (Part Agent-PA).Its task is to go by the graph from the source node to the destination node in given conditions and follow a path corresponding to the demanded technologi-cal operations[2].The conditions(the quickest,the cheapest treatment,etc.)are known to a given agent PA and are a basis for realizing a routing process of choosing dynamically the proper path.The payment may be done–directly:For the purpose,each node(exactly-output port on an edge)has an accountopened.Appropriate sums may be paid into the account by the treated part,Each processed part which is to be treated in the system has its account andtransfers an appropriate payment for sending to an account of a sending portfor the treatment by the corresponding edge.Agents authorized to use the founds accumulated on accounts may perform alloperations on the accounts with the intermediary of network operations.–indirectly:In a simplified case,a sending port obtains a generalized payment in a formof some number of(stipulated)points.The number of points collected makesevidence on activity of a given port,and what follows-a given operation(treat-ment)corresponding to the edge in a graph.The points collected may then be changed into cash and transferred from thefunds,(e.g.centrallyfixed),planned to be spent on production system func-tioning or development.The points collected may then be used as a basis to take decisions concerningthe optimal functioning and development of production system.The payments make a basis for market oriented decisions related to modifications (extension,decrease)of certain parts of a production system.Decisions are undertaken and then realized by the multi-agent system managing the production system.3Multi-agent system managing the production systemThe production system is dynamically mapped into the model(of the form of graph). The model is managed by the multiagent system.The multi-agent system consists of two main parts:a population of different kinds of agents and the environment in which the agents work.The environment is determined by the model of the production sys-tem,and may be considered as a particular graph(Fig.3).The model is created and dynamically updated to take into consideration the circulation of the treated parts in the system.There are the following types of agents in the system:–Input Part Agent(IPA)that provides the interface between the external reality and production system and creates the Part Agent.–Path Agent(PthA)that provides the proper path selection for the treated part.–Path Messenger Agent(PMA)that collects and transfers information around the graph about available operations(treatments)in the system.CEEMAS’01,26-29September2001,Krak´o w,Poland55–Port Agent(PoA)that makes possible for agents to leave a given node to reach the neighboring one by the selected edge,and to be treated by an operation represented by the edge.–Part Agent(PA)that represents a given treated part in the system,and provides a transfer of a given part from the beginning to the end following the proper path(i.e.sequence of operations)in the system.–Linker Agent(LA)that collects necessary information around the graph(or its part)to enable detection of a problem of traffic in the graph(such as overloaded operations,waiting lines for operations etc.).–Investor Agent(IA)that enables to undertake decisions with investment in the graph(extension or reduction of connections,new operations etc.)to modify the production system layout.–Investor Proxy Agent(IPxA)that participates in negotiations concerning invest-ments in the graph.Fig.4.Schema of the graph node where the agent undertake an action to establish new itinerary continuationThe real time optimal system management consists of a properly composed path for the treated part navigation in the production system ing its own required sequence of operations Part Agent goes across the graph from the beginning node to the ending node.At every node it enters the where cooperating with PthA,PMA,LA agents it obtains information about the possibilities of the path con-tinuation.Accordingly to its need(cheapest,quickest)it selects the best continuation of the itinerary and goes to the appropriate port where it pays(cooperation with PoA agent)for entering the following edge in the graph(which means that the corresponding operation will be executed on the part)The decisions concerning investments in the production process layout(which re-sults in the layout modifications)are settled as a result of negotiations among Investors and Investor Proxy agents(IA,IPxA).We can consider that the edge linking node with the node is overloaded(it means that the technological operations-drills be-tween and has insufficient efficiency rate).A scenario of the action undertaken by agents to establish a new connection(or to raise operation rate)may consists of the following steps(fig.5):–Port at the node linking it with the node is overloaded and the agent AoP corresponding to the port creates the LA agent that is sent to the node.562nd International Workshop of Central and Eastern Europe on Multi-Agent SystemsFig.5.Schema of the graph where the agent undertake action to establish new edges(operations, treatments)–At the node the AL agent verifies the traffic linking the node with the node.–If the traffic from the node to the node going via the node is too heavy, the agent Linker goes back to the node with a proposition to create a direct link between the nodes and,or looks for the next overloaded node(for example node).This proposition is paid to the Investor agent at the node.–Investor agent at the node builds afinancial project of the enterprise,and sends the Investor-Proxy agent to the node to negotiate its participation in the project.–If the negotiations are completed successfully the agent Investor-Proxy realizes the connection(direct or complex)placing order for the appropriate service with the appropriate company.4Interactions in the multi-agent system for the production system managementAn interaction protocol is a set of rules of interaction that describes what action each agent can take at each time.In this chapter typical interaction protocols used in the multi-agent systems are presented,then a presentation is given how these protocols can be applied to the interactions in the system for the production process management, which was presented in the previous chapter.CEEMAS’01,26-29September2001,Krak´o w,Poland57 4.1The interactions protocols used in the multi-agent systems.Contract net.Its idea is based on the mechanism of contracting,used in business[12]. There are two roles of the agents in the protocol:manager–agent,which orders the realization of the task.and contractors–the agents–potential executors of the task.Voting In some situations the agents can hold a vote on certain decisions.The result of the voting is obligatory for all agents participating in the voting.In[11]some protocols of voting are presented:plurality protocol,binary protocol and Bord’s protocol.Auctions The exchange of goods between the agents may be realized by auction.The agents participating in the auction can perform one of the followings roles:auctioneer or participant of the auction.There are many different protocols of auctions which are associated with particular strategies of the agents.On the basis of an overview from[10] we can enumerate:English(first-price open cry)auction,Vickrey auction(second-price sealed-bid),all-pay auction.Market interactions The interactions based on the market approach have several advan-tages,such as well analyzed(in economic science)theory and a low cost of communica-tion.There are two kinds of agents in the system:producers and consumers.Producers transform the goods using technologies they have and the consumers put demands for the goods needed and their decisions are undertaken on the basis of their utility function [15,4].The market approach was used for creation of systems strictly connected with the management of the graph or network:–the system for analysis of the distributed multi-commodityflow problem[14],–finding the optimal localization of the mirrors of services in the Internet[8],–system of routing of the packages in the Internet based on market mechanisms[6].4.2The types of the interactions in the multi-agent system for the managementof the product ion process.Introduction This sub-chapter will analyze what interactions protocols can be chosen for particular tasks of the production management system.We concentrate on the fol-lowing problems:–choosing the Port by PA agent,which enables the best quality of the connection (using the criterion based on the cost and/or the performance of the treatment),–gaining information about the state of the graph,–adding or removing the links to/from the graph by Investor and Investor Proxy.Choosing the Port by Part Agent The process of choosing the Port by the Part Agent may be realized using the following interactions protocols:–Contract Net–PA agent informs all Ports on the Node that it wants to get a defined treatment(which means that it is looking for a particular node in the graph).The Ports inform what price it has to pay for the available treatment,and what are other properties of the treatment(time elapsed,quality etc.)The PA agent chooses the best from its point of view of treatment–it means the corresponding Port.582nd International Workshop of Central and Eastern Europe on Multi-Agent Systems –voting–The PA agents vote which treatment is the best and the corresponding edge gets a privilege to be selected for such treatment.–auction–If the production system is heavily loaded(overloaded),the PA agents can take part in the auctions of treatment offered by the Ports(i.e.edges).The opposite situation is also possible–the agent PA demands to buy the treatment offered by one of the Ports at a given node.There are more than one available ports offering similar kinds of treatment and the Ports participate in the auction offering lower and lower prices and better treatment.The PA agent chooses the lowest price, the best treatment.This algorithm may be used if the production system is currently not loaded.–market–The complex negotiation of the cost of the treatment between PA agents considered as consumers and Ports playing a role of producers can be implemented using a market model.Collecting the information about the current state of the network.Linker Agent can gain the information about the state of the network using the following interaction pro-tocols:–Contract Net–LA announces that it is ready to buy information about the state of the network.The group of the nodes(Node Managers)can send their proposals concerning the quality and the costs of the possessed information.–Market–LA and Port Agents participate in the exchange of the information about the structure of the network.The prices of the information are set by the market methods.The modification of the structure of the production process structure(graph)-adding or removing the operations(edges)The modification of the structure of the network by adding or removing the edges representing technological operations can be performed using the following interaction protocols:–Auction–Agents Investors take part in the auction of adding a new edge to the graph or selling the one by the Investor who actually owns it.It is also possible to increase or reduce the efficiency,productivity or quality of a given operation(edge).–Market–The Investors can buy or sell technological operations(edges).5Simulation of the multi-agent system for the production system layout(graph management)The multi-agent system proposed for the production system layout management has been modelled and verified by simulation(fig.6).It has a limited function–provides only the migration of the Part Agents using the Contract Net Protocol.Other tasks,such as collecting information about the structure of the network and changing the structure of the network(adding/removing nodes)are not realized.The following interaction among the agents has been realized:CEEMAS’01,26-29September2001,Krak´o w,Poland59Fig.6.Schema of the examined production system layout-graph corresponding to the system:a -at the beginning of simulation,b-at the end of simulation after the improvement of the system layout(extension of operations-corresponding edges)–Part Agent when arriving at a given node tries to reach the destination node by the edge with demanded operation at the lowest costs.–At the node a number of options of transfer continuation at different prices are proposed to the Part Agent.–Part Agent accepts the solution,taking into consideration the price of the following operation versus its own resources and the quality of the treatment versus the time it can waste for all the operations from the beginning to the end of the part treatment.–Part Agent pays the Port Agent for the treatment.With the resources saved the Port Agents(and Node Manager)undertake modification of the production system layout(graph).The simulation of the system was performed on the computer network composed of 6nodes with edges representing technological operations.Technology needs and pricing policy favour the following edges of the graph: (B,F),(B,G),(C,G),(D,G),(E,F),(E,G),(F,G).The initial state of the network with the same productivity rate of all edges(technological operations)is shown infig.6a. After a number of operations execution and transfers of parts in the system the nodes using collected funds modify the parameters of the network.The result of the system investment is presented infig.6b.After the improvement(changes of the efficiency of operations corresponding to the edges)realized by the system the overloaded operations (edges)were reinforced(thick lines in thefig.6b).6ConclusionThe proposed system of a automatic management of production system enables both optimal real-time management of the production system functioning,and optimal,self-acting adaptation of the network structure to users’needs,also in real-time[3].Both aspects of optimal management,considered together may bring very interesting results. The application of the above mentioned system will lower costs of production system exploitation.In the future it will enable the realization of optimal management of pro-duction systems layout for large,extensive production systems.The proposed system602nd International Workshop of Central and Eastern Europe on Multi-Agent Systems enablesflexible production systems management(of graph structure)with the use of economic criteria of optimization.The system takes advantage of possibilities given by the decentralized,multi-agent system,that enables making local decisions,taking into account local,and if necessary and possible-a global point of view.The presented system may be easily extended to uniform,optimal management of network,including not only hardware management but also other resources,-work software.References1.K.Cetnarowicz,E.Nawarecki,and M.Zabinska.M-agent architecture and its applicationto the agent oriented technology.In Proc.of the DAIMAS’97.International workshop:Dis-tributed Artificial Intelligence and Multi-Agent Systems.St.Petersburg,Russia,1997.2.K.Cetnarowicz,M.˙Zabi´n ska,and E.Cetnarowicz.A decentralized multi-agent systemfor computer network reconfiguration.In J.Stefan,editor,Proc.of the21st International Workshop:Advances Simulation of Systems-ASSIS’99.MARQ,Ostrava,Czech Republic, 1999.3.Krzysztof Cetnarowicz and Jaroslaw Kozlak.Multi-agent system for decentralized com-puter network management.In Proceedings of the Second Conference on Management and Control of Production and Logistics(MCPL’2000),Grenoble,July2000.4.John Q.Cheng and Michael P.Wellman.The WALRAS algorithm:A convergent distributedimplementation of general equilibrium putational Economics.,1997.5.N.Gaither.Production and Operations Management.The Dryden Press,New York,1994.6.M.A.Gibney and N.R.Jennings.Dynamic resource allocation by market based routingin telecommunications networks.In Garijo F.J.Albayrac S.,editor,Intelligent Agents for Telecommunications Applications(IATA’98).Springer Verlag,1998.7.G.Dobrowolski K.Cetnarowicz,E.Nawarecki.Multi-agent mrp class system for productionand disassembling.In Management and Control of Production and Logistics.IFIP,IF AC, IEEE Conference,Grenoble,France,2000.ENSIEG,LAG Grenoble,France2000.8.Tracy Mullen and Michael P.Wellman.A simple computational market for network infor-mation services.In ICMAS.1995.9.J.F.Proud.Master Scheduling.John Wiley&Sons Inc.,New York,1994.10.Tuomas W.Sandholm.Negotiation among self-interested computationally limited agents.PhD thesis of University of Massachusetts Amherst,1996.11.Tuomas W.Sandholm.Distributed rational decision making.In Gerhard Weiss,editor,Multi-agent Systems.A Modern Approach to Distributed Artificial Intelligenc,volume II,chapter7, pages201–258.The MIT Press,1999.12.Reid G.Smith.The contract net protocol:High-level communication and control in a dis-tributed problem solver.IEEE Transactions on Computers.,C-29(12):1104–1113,1980. 13.W.J.Stevenson.Production/Operations Management.Richard D.Irwin Inc.,HomewoodIL60430,1990.14.Michael P.Wellman.A market-oriented programming environment and its application todistributed multicommodityflow problem.Journal of Artificial Intelligence Research,pages 1–23,1993.15.Michael P.Wellman.Market-oriented programming:some early lessons.In S.Clearwa-ter,editor,Market-Based Control:A Paradigm for Distributed Resource Allocation.World Scientific,1996.。

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