基于移动Agent的自动协商应用

主要研究电子商务中的协商问题,为实现自动协商提供了一个可行的方向。通过分析目前电子商务的协商现状及所存在的问题,提出将移动Agent技术引入电子商务,为B2C电子商务中一对多的协商问题自动化指出了一个实现方向,并得出基于移动Agent的自动协商系统的建设目标及特点。这将对于实现电子商务协商的自动化,提供有力的理论支持及发展方向。     

基于Agent和博弈分析的B2B协商系统模型研究

针对B2B电子商务在交易自动化和智能化方面的不足,提出了一种融合多Agent和鲁宾斯坦讨价还价博弈的B2B电子商务协商系统模型。在多Agent环境下,该系统可以降低网络的通信流量,提高交易效率,实现交易的自动化和智能化;鲁宾斯坦讨价还价博弈为协商系统提供了具体的协商策略。对B2B协商系统模型的协商流程、协商算法以及协商结果进行了讨论。     

多Agent自动协商策略和算法

针对传统协商过程周期长、代价高的问题,提出了三类形式化的曲线簇来描述Agent所采取的协商策略以及一种数学化和计算机程序化的多Agent自动协商算法。这三类策略分别为急躁型、节俭型和折中型。通过实验模拟发现采用所提出的协商策略和算法更符合人类思维的直观。该方法可应用于多种电子商务交易（如B2B和B2C）等活动中。     

P2P模式在电子商务资源管理中的应用

对电子商务系统与网格技术进行了分析,提出了一种基于P2P的B2B电子商务资源管理方案。采用应用层、服务层和资源层三个不同的层次进行电子商务资源管理。基于网格技术OGSA架构,实现了cnXML电子商务中用户注册、信息发布与查询过程。     

一种基于XML的贸易协商协议

在商业贸易过程中,制定合同或者订单之前,贸易双方的协商是个很重要的环节。如果要在电子商务中实现这个过程,那么如何描述贸易双方的需求以及如何解决贸易双方需求之间的矛盾是关键所在。该文提出了一种基于XML的贸易协商协议和对应的系统框架以解决这个问题,从而在电子商务中实现了贸易协商过程的自动化和标准化。     

基于Agent的电子商务博弈协商过程的研究

把人工智能的Agent技术和经济学的不完全信息动态博弈方法结合起来,在买方的立场上通过约束满足机制实现了自动协商的过程。该方法及自动协商过程可用于解决电子商务协商领域中的协商效率不高的问题。     

基于事例推理技术的电子商务自动协商研究

基于经验的协商策略利用相似性原理对实例进行匹配,以指导协商过程。本文提出了一个应用CBR技术建立电子商务自动协商模型,及相应的协商实例库的设计和检索。     

你省钱 我成功

通过整合资源、技术升级后,不久前“拉拉手”电子商务网(www.lalasho.com)推出了以B2B为主的电子商务新模式——企业运营资源交易网。拉拉手依托于焦点网(www.focus.com.cn)的强大门户网络背景,1999年12月1日推出时,其商务模式、市场前景颇为业界瞩目,产生了较大的反响。 基于B2B的拉拉手电子商务网(www.lalasho.com),是一个企业运营资源交易网,目的是为企业提供多种网上商务服务,整合企业内外部各种资源。所谓企业运营资源,是指企业运营过程中所需的非生产原料的产品和服务。其中产品是     

B2B异构环境中实现信息数据的共享

本文论述了目前B2B电子商务信息数据交换过程中所存在的问题,并探讨了利用J2EE平台结合Web Services技术解决B2B电子商务异构环境中信息交换问题的可行性及其优势.这一方案可以有效地解决跨平台B2B信息交换中的异构问题,提高信息交换的效率,实现企业应用的分布式处理,将企业内外部数据资源进行整合与共享.     

B2B异构环境中实现信息数据的共享

本文论述了目前B2B电子商务信息数据交换过程中所存在的问题,并探讨了利用J2EE平台结合Web Services技术解决B2B电子商务异构环境中信息交换问题的可行性及其优势.这一方案可以有效地解决跨平台B2B信息交换中的异构问题,提高信息交换的效率,实现企业应用的分布式处理,将企业内外部数据资源进行整合与共享.     

基于商务谈判的机器学习理论方法研究

电子商务的谈判中应用机器学习方法可增加其智能特性.探讨基于商务谈判的机器学习理论,建立了商务谈判学习模型,设计出相应的学习算法Strategy Learning以获取谈判领域的专家知识-谈判规则.通过一个应用实例,验证了SL方法的可行性和正确性.     

An economics-based negotiation scheme among mobile devices in mobile grid

In this paper, we propose an economics-based distributed negotiation scheme among mobile devices in mobile grid. In our model, there are energy negotiation and transactions between buyer devices and seller devices. Dynamic allocation of energy resources in mobile grid is performed through online transactions within markets. Mobile devices can be sellers and buyers that use optimization algorithms to maximize predefined utility functions during their transactions. Seller device agents sell the underlying energy resources of the mobile device. Buyer device agent makes buying decisions within the budget constraints to acquire energy resources. An economics-based negotiation algorithm among mobile devices is proposed. The proposed algorithm decomposes mobile grid system optimization problem into a sequence of two sub-problems. In the simulation, the performance evaluation of economics-based negotiation algorithm is evaluated.     

Towards a web services and intelligent agents-based negotiation system for B2B eCommerce

With the explosive growth of the number of transactions conducted via electronic channels, there is a pressing need for the development of intelligent support tools to improve the degree and sophistication of automation for eCommerce. With reference to the BBT business model, negotiation is one of key steps for B2B eCommerce. Nevertheless, classical negotiation models are ineffective for supporting multi-agent multi-issue negotiations often encountered in eBusiness environment. The first contribution of this paper is the exploitation of Web services and intelligent agent techniques for the design and development of a distributed service discovery and negotiation system to streamline B2B eCommerce. In addition, an effective and efficient integrative negotiation mechanism is developed to conduct multi-party multi-issue negotiations for B2B eCommerce. Finally, an empirical study is conducted to evaluate our intelligent agents-based negotiation mechanism and to compare the negotiation performance of our software agents with that of their human counterparts. Our research work opens the door to the development of the next generation of intelligent system solutions to support B2B eCommerce.     

基于加速遗传模拟退火算法的协商策略研究

随着电子商务的不断发展,如何快速有效地进行自动协商是研究所面临的一个重要问题。根据Bazzar协商模型,提出了一种加速遗传模拟退火算法(AGASA),该算法将遗传算法和模拟退火算法结合,并且加入压缩搜索范围的算法加速机制,同时采用特殊的实数编码方式令算法能更加稳定地收敛。仿真试验表明,算法能快速稳定地解决协商模型所描述的协商问题。     

防空监视网络传感器资源的动态协商分配

针对防空监视网络的传感器管理问题,讨论了传感器资源的多代理动态协商分配方法。结合防空监视网络传感器的功能,提出了传感器代理协商分配传感器资源的协商协议机制,设计了用于传感器资源协商分配的3个协议并且仿真评价了协议的应用条件。传感器资源的动态协商分配,充分利用了传感器代理的自治性和协作性,能够满足实现防空监视网络传感器资源的动态分配的实时性和准确性要求。     

供应链环境下基于库存约束的多agent协同谈判研究

建立了供应链环境下的多agent协同谈判模型,用于制造商同时与多个下游企业进行动态谈判。对谈判的基本要素进行了描述,探索了这种谈判的具体流程,并提出了一种基于库存约束的谈判策略,最后用原型系统进行了验证。因为协同谈判是在整体的库存约束条件下进行的,谈判结果也是经过管理agent从整体的观点进行评估后形成的,所以制造商在履行订单协议时,不会发生内部资源冲突,提高了订单的履行率。     

An ontology based approach to organize multi-agent assisted supply chain negotiations

Effective supply chain management (SCM) comprises activities involving the demand and supply of resources and services. Negotiation is an essential approach to solve conflicting transaction and scheduling problems among supply chain members. The multi-agent system (MAS) technology has provided the potential of automating supply chain negotiations to alleviate human interactions. Software agents are supposed to perform on behalf of their human owners only when equipped with sophisticated negotiation knowledge. To better organize the negotiation knowledge utilized by agents and facilitate agents’ adaptive negotiation decision making ability, an ontology-based approach is proposed in this paper. Firstly, the multi-agent assisted supply chain negotiation scheme is presented to configure the general design components of the negotiation system, covering the agent intelligence modules, the knowledge organization method and the negotiation protocol. Then, the ontology-based negotiation knowledge organization method is specified. The negotiation knowledge is separated into shared negotiation ontology and private negotiation ontology to ensure both the agent communicative interoperability and the privacy of strategic knowledge. Inference rules are defined on top of the private negotiation ontology to guide agents’ reasoning ability. Through this method, agents’ negotiation behaviors will be more adaptive to various negotiation environments utilizing corresponding negotiation knowledge.     

REACTIVE TUNING OF TARGET ESTIMATE ACCURACY IN MULTISENSOR DATA FUSION

Dealing with conflicting and target-specific requirements is an important issue in multisensor and multitarget tracking. This paper aims to allocate sensing resources among various targets in reaction to individual information requests. The proposed approach is to introduce agents for every relevant target responsible for its tracking. Such agents are expected to bargain with each other for a division of resources. A bilateral negotiation model is established for resource allocation in two-target tracking. The applications of agent negotiation to target covariance tuning are illustrated together with simulation results presented. Moreover, we suggest a way of organizing simultaneous one-to-one negotiations, making our negotiation model still applicable in scenarios of tracking more than two targets.     

STRATEGIC NEGOTIATION FOR SHARING A RESOURCE BETWEEN TWO AGENTS*

In this article, we propose a strategic negotiation model that enables self‐motivated rational agents to share resources. The strategic negotiation model takes the passage of time during the negotiation process itself into account. The model considers bilateral negotiations in situations characterized by complete information, in which one agent loses over time whereas the other gains over time. Using this negotiation mechanism, autonomous agents apply simple and stable negotiation strategies that result in efficient agreements without delay, even when there are dynamic changes in the environment. Simulation results show that our mechanism performs as well as a centralized scheduler and also has the property of balancing the resources' usage.     

基于加速模拟退火策略的Agent协商优化算法

网络的普及促进了电子商务的发展，而智能代理是电子商务的关键，如何实现智能代理的快速、高效协商是一个很重要的问题。本文在加速模拟退火策略的基础上，提出了一种Agent协商优化算法。理论分析和仿真实验都表明该算法是一个快速、有效的方法。