基于多智能体的虚拟企业环境下自治agent的协商

讨论了基于多智能体(Multi-agent)的虚拟企业环境下自治agent的协商,针对虚拟企业环境下协商的特点,提出了虚拟企业环境下自治agent的协商通用形式化模型,并在模型的基础上给出了一系列协商策略与协商战略.使用这些策略与战略不仅可以帮助agent生成建议与反建议,而且可以帮助agent对收到的建议做出评价,以作为生成反建议或终止协商的依据.     

基于多Agent协商的虚拟企业伙伴选择方法

伙伴选择是虚拟企业建立过程中的核心问题,分析了虚拟企业的特点、虚拟企业环境下协商问题的特点,提出了一个适合于虚拟企业环境的多Agent协商模型。该模型支持多Agent多议题的多轮谈判,并将Agent类型引入到协商中来,作为指导协商Agent提议的一个重要因素。在不完全信息的条件下,应用贝叶斯学习的方法,更新既有信息,并通过分析对方Agent的历史提议序列,推测其类型,来指导自身的提议策略和战术,使自己的提议更具有针对性,避免了盲目性,从而节约协商时间,提高了协商的效率,使得盟主企业能在尽短的时间里寻找到理想的合作伙伴。     

基于MAS的虚拟企业建模研究

虚拟企业建模可以指导虚拟企业的快速创建，实现运作过程的优化，是开发虚拟企业信息系统的基础，虚拟企业的分布、动态、异构等特点使其建模问题具有一定的复杂性．在分析虚拟企业建模的特点和国内外研究基础上，提出了基于多智能体系统面向合作伙伴选择与敏捷车间调度的虚拟企业两层框架结构模型，描述了总体框架和系统中Agent通用结构，并结合实例描述了实现本模型的UML面向对象建模方法．     

一种基于多代理技术实现的虚拟企业中的敏捷调度

提出了一种用多代理技术和基于协商的策略解决虚拟企业调度冲突的方法。首先介绍了虚拟企业中敏捷调度的特性及调度冲突的产生,分类及解决方法,并描述了调度、计划、协商、监督执行之间的互操作,通过代理的智能,自治等特性可以较好地实现调度的敏捷性,最后给出了在一个面向虚拟企业的信息集成系统ViaSopc中基于多代理技术实现的基于协商的敏捷调度模型。     

基于生命科学原理的虚拟企业协同工作研究

虚拟企业的动态重组和协同工作是实施和运行虚拟企业的核心技术。该文学习生命科学的原理,提出了面向CSCW的分布式协同的虚拟企业的体系结构和虚拟企业协同工作的多智能体系统模型。原型系统验证了该文所提出的体系结构和模型是正确可行的。     

基于 RBF 神经网络和强化学习算法的供应链产销协同计划冲突消解研究

为提高传统协商自学习能力,利用多 agent 智能技术,建立基于黑板模型的协商框架,构建五元组协商模型,采取 Q-强化学习算法,给出一种协商策略;使用 RBF 神经网络进一步优化协商策略,预测对手信息并调整让步幅度。通过算例验证该方法的可行性和有效性,通过与未改进的 Q-强化学习算法对比,该方法可增强协商agent 的自学习能力,缩短协商时间,提高冲突消解效率。     

基于Multi-Agent的虚拟企业集成框架研究

本文采用多智能体技术构建虚拟企业集成框架 ，在该框架中，由agent代表各成员企业，相应地，多智能体系统(Multi-Agent System)代 表虚拟企业．其中重点提出了agent通用结构，它包括核心层、接口、通讯层三个组成部分 ：核心层由企业的原有系统构成，以保证企业的自治性；接口实现了agent核心层与通讯层 之间的信息交换；通讯层则支持agent之间的交互，实现企业间的合作．该虚拟企业集成框 架不仅支持虚拟企业像一个企业一样进行工作，而且实现了虚拟企业的可重用、可重构和可 扩充，并使企业根据需要同时参加多虚拟企业成为可能．     

一种协商在线增量学习算法及其在基于智能体的电子商务中的应用

随着多智能体系统MAS的迅猛发展,常常需要进行在线的协商。然而由于协商中不完全信息的存在,常常会大大影响协商的效果。该文提出一种多智能体协商中的动态在线增量学习算法,采用Q-学习机制来学习agent协商中的不完全信息。该文将这种学习算法应用基于智能体agent的电子商务中。实验证明算法可以加速协商的过程,提高协商的效果。     

基于M-Agent单边与多边协商策略的虚拟企业协作机制研究

在分析了采用合同网/市场机制模式构建的虚拟企业协作体系的局限性基础上,基于MAS单边与多边协商策略模型,提出了一种在企业任务划分不明确状态下的虚拟企业协作机制和评价方法.详细论述了基于学习Agent单边协商策略的盟主企业与成员企业间的单边协商过程和选择机制,以及潜在企业联盟中,基于Agent多边协商策略的成员企业间的多边协商过程.最终选择最有利的联盟结构,以保证虚拟企业的全局利益最大化和盟主企业与成员企业间的满意收益.     

基于RMM 和贝叶斯学习的城市交通多智能体系统

提出一种基于递归建模方法（RMM）的多智能体协调方法,使智能体在分布环境下对其它智能体的决策建模选择合理的行动。对RMM中的信念更新采用贝叶斯学习方法,使智能体可以确定其它智能体的准确模型并实时更新信息。在城市交通控制领域建立多智能体交通控制系统,仿真结果表明了该方法的有效性,对实现智能交通系统具有重要意义。     

An empirical study of interest-based negotiation

While argumentation-based negotiation has been accepted as a promising alternative to game-theoretic or heuristic-based negotiation, no evidence has been provided to confirm this theoretical advantage. We propose a model of bilateral negotiation extending a simple monotonic concession protocol by allowing the agents to exchange information about their underlying interests and possible alternatives to achieve them during the negotiation. We present an empirical study that demonstrates (through simulation) the advantages of this interest-based negotiation approach over the more classic monotonic concession approach to negotiation.     

Service-oriented architecture for ontologies supporting multi-agent system negotiations in virtual enterprise

This paper offers a service-oriented architecture (SOA) for ontology-based multi-agent system (MAS) negotiations in the context of virtual enterprises (VEs). The objective of this paper is fourfold. First, it is to design a SOA which utilizes ontology and MAS to provide a distributed and interoperable environment for automated negotiations in VE. In this architecture, individual ontologies for both the VE initiator and its potential partners are constructed to describe and store resources and service knowledge. Second, a series of semantic ontology matching methods are developed to reach agents’ interoperability during the negotiation process. Third, correspondence-based extended contract net protocol is presented, which provides basic guidelines for agents’ reaching mutual understandings and service negotiation. Last, a fuzzy set theory based knowledge reuse approach is proposed to evaluate the current negotiation behaviors of the VE partners. A walkthrough example is presented to illustrate the methodologies and system architecture proposed in this paper.     

Towards a multi-agent system for regulated information exchange in crime investigations

This paper outlines a multi-agent architecture for regulated information exchange of crime investigation data between police forces. Interactions between police officers about information exchange are analysed as negotiation dialogues with embedded persuasion dialogues. An architecture is then proposed consisting of two agents, a requesting agent and a responding agent, and a communication language and protocol with which these agents can interact to promote optimal information exchange while respecting the law. Finally, dialogue policies are defined for the individual agents, specifying their behaviour within a negotiation. Essentially, when deciding to accept or reject an offer or to make a counteroffer, an agent first determines whether it is obligatory or permitted to perform the actions specified in the offer. If permitted but not obligatory, the agent next determines whether it is in his interest to accept the offer.     

Fuzzy agent-based expert system for steel making process

In this paper, a fuzzy multi-agent system is proposed for electric arc furnace steel making processes. Each process of electric arc furnace steel making is assigned to be an agent, which works independently whilst coordinates and cooperates with other acquaintance agents. Adaptive neuro-fuzzy inference system is used to generate agents’ knowledge bases. Contract net protocol is used as negotiation protocol in the proposed multi-agent system.     

A protocol for multi-agent negotiation in a group-choice decision making process

Negotiation is one of the most important features of agent interactions found in multi-agent systems, because it provides the basis for managing the expectations of the individual negotiating agents, and it enables selecting solutions that satisfy all the agents as much as possible. In order for negotiation to take place between two or more agents there is need for a negotiation protocol that defines the rules of the game; consequently, a variety of agent negotiation protocols have been proposed in literature. However, most of them are inappropriate for Group-Choice Decision Making (GCDM) because they do not explicitly exploit tradeoff to achieve social optimality, and their main focus is solving two-agent negotiation problems such as buyer–seller negotiation. In this paper we present an agent negotiation protocol that facilitates the solving of GCDM problems. The protocol is based on a hybrid of analytic and artificial intelligence techniques. The analytic component of the protocol utilizes a Game Theory model of an n-person general-sum game with complete information to determine the agreement options, while the knowledge-based (artificial intelligence) component of the protocol is similar to the strategic negotiation protocol. Moreover, this paper presents a tradeoff algorithm based on Qualitative Reasoning, which the agents employ to determine the ‘amount’ of tradeoff associated with various agreement options. Finally, the paper presents simulation results that illustrate the operational effectiveness of our agent negotiation protocol.     

基于熟人联盟及扩充合同网协议的多智能体协商模型

合同网协议可扩充性好，处理动态环境能力强，在多智能体系统协商中应用广泛．在分析了经典合同网协议的优缺点后，提出了基于熟人联盟及扩充合同网协议的多智能体系统协商模型．根据模型设计了适合扩充合同网协议的系统结构，引入了熟人联盟以及信任度参数，提出熟人联盟生成方法及信任度更新规则并构造了基于经典合同网协议的扩充合同网协议．最后通过对一个导弹防御例子的测试及分析，证实了该模型在保证协商质量的基础上，有效地降低了协商代价．     

A knowledge intensive multi-agent framework for cooperative/collaborative design modeling and decision support of assemblies

Multi-agent modeling has emerged as a promising discipline for dealing with decision making process in distributed information system applications. One of such applications is the modeling of distributed design or manufacturing processes which can link up various designs or manufacturing processes to form a virtual consortium on a global basis. This paper proposes a novel knowledge intensive multi-agent cooperative/collaborative framework for concurrent intelligent design and assembly planning, which integrates product design, design for assembly, assembly planning, assembly system design, and assembly simulation subjected to econo-technical evaluations. An AI protocol based method is proposed to facilitate the integration of intelligent agents for assembly design, planning, evaluation and simulation process. A unified class of knowledge intensive Petri nets is defined using the O-O knowledge-based Petri net approach and used as an AI protocol for handling both the integration and the negotiation problems among multi-agents. The detailed cooperative/collaborative mechanism and algorithms are given based on the knowledge objects cooperation formalisms. As such, the assembly-oriented design system can easily be implemented under the multi-agent-based knowledge-intensive Petri net framework with concurrent integration of multiple cooperative knowledge sources and software. Thus, product design and assembly planning can be carried out simultaneously and intelligently in an entirely computer-aided concurrent design and assembly planning system.     

一种基于Multi-Agent的供应商选择系统

构建了一种基于Multi-Agent的供应商选择系统,给出了其中各主要组成部分的结构和功能,并提出了一种基于Vickrey拍卖的改进的合同网协议,作为制造商Agent和供应商Agent的协商策略,最后描述了他们之间的交互过程.     

The impact of available information on negotiation results

Negotiation is the most famous tool for reaching an agreement between parties. Usually, the different parties can be modeled as a buyer and a seller, who negotiate about the price of a given item. In most cases, the parties have incomplete information about one another, but they can invest money and efforts in order to acquire information about each other. This leads to the question of how much each party will be willing to invest on information about its opponent, prior to the negotiation process. In this paper, we consider the profitability of automated negotiators acquiring information on their opponents. In our model, a buyer and a seller negotiate on the price of a given item. Time is costly, and incomplete information exists about the reservation price of both parties. The reservation price of the buyer is the maximum price it is willing to pay for an item or service, and the reservation price of the seller is the minimum price it is willing to receive in order to sell the item or service. Our research is based on Cramton’s symmetrical protocol of negotiation that provides the agents with stable and symmetric strategies, and involves a delay in proposing an offer for signaling. The parties in Cramton’s model delay their offers in order to signal their strength, and then an agreement is reached after one or two offers. We determine the Nash equilibrium for agents that prefer to purchase information. Then, in addition to the theoretical background, we used simulations to check which type of equilibrium will actually be obtained. We found that in most of the cases, each agent will prefer to purchase information only if its opponent does. The reason for these results lies in the fact that an agent that prefers to purchase information according to a one-side method, signals its weakness and thereby reduces its position in the negotiation. Our results demonstrate the efficiency of joint information acquisition by both agents, but they also show that one-sided information purchasing may be inefficient, if the acquisition activity is revealed by the opponent, which causes it to infer that the informed agent is relatively weak.     

Agent-based optimisation of logistics and production planning

Manufacturing and logistics service provision enterprises are currently moving towards open virtual enterprise collaboration networks to meet the needs of the Global Economy. In such networks, manufacturing and logistics planning and scheduling is challenging due to the difficulties in integrating information from different partners and in exploring a large and dynamically changing number of planning and scheduling alternatives. Agent-based technology is considered suitable to support planning and scheduling in such enterprises because agents can dynamically adapt their behaviour to changing requirements and they can reduce the number of planning and scheduling alternatives via negotiation.This paper presents an agent-based approach for supporting logistics and production planning, taking into account not only production schedules but also availability and cost of logistic service providers. This is achieved through efficient negotiation mechanisms based on an extended contracting protocol. The agent infrastructure is being developed within the context of Agentcities, a successful EU-funded initiative to build a world-wide distributed and open platform which provides agent-based services.The proposed approach is illustrated in a case study concerning optimisation of production planning of a virtual manufacturing enterprise in relation to sub-contracted logistic services used to transport materials between the enterprise units.