一种基于上下文协商的动态服务组合方法

普适计算的计算环境和交互信息动态变化,为了提供适时适地的服务,服务组合除了满足用户的需求之外,还要适应环境的变化.以面向普适计算的分布式文档管理系统为例,提出一种基于上下文协商的动态服务组合方法,适应普适计算环境下资源动态变化的特点,同时满足用户对服务的需求.首先定义上下文和带有上下文信息的服务模型;然后给出服务和设备以及服务和用户之间的上下文协商约束条件,根据约束条件提出基本算法实现服务动态组合,并对基本算法进行优化;最后通过原型系统和实验验证算法的性能和有效性,并通过实验数据分析上下文对于服务组合的影响.实验数据说明:提出的方法能够应用在普适计算环境中上下文敏感的服务组合问题上,提高服务组合的动态适应性和网络资源利用率.     

A Brokerage Framework for Internet Commerce

Electronic brokerage is regarded as a core functionality in overcoming many current limitations of Internet commerce. In this paper, we describe current approaches to brokerage services, both, on the Internet and for distributed object systems and analyze how brokerage influences business models for electronic commerce. We then describe OFFER, a project that focuses on advanced models for brokerage on the Internet. We investigate a variety of brokerage services and provide a high-level specification for core components of a generic brokerage service on the Internet. The work should provide a framework to classify existing approaches and a roadmap for further research in this emerging field.     

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.     

网络交易中多Agent协商策略的研究

网络购物是当今社会发展的必然趋势,如何在丰富的网络资源中选择自己需求的商品达成交易是关键。因此根据Agent技术的特点,采用Agent技术对网络资源进行收集、选择、提取,获得用户满意的商品信息,并提出了一种多Agent协商策略模型,收到对方提出的意见后通过经验值和互助机制作出一定的判断,看是否达到预期的效果给出相应的反映。该模型主要通过经验值的积累,准确掌握对方的信息,制定出一套协商策略,采用利益随机调整方式选择对策,促进协商成功。经过实验证明,此算法有效。     

Development and evaluation of the many-to-many supplier negotiation strategy

Supply chain is a complex logistics network, consists of suppliers, manufacturers, warehouses, distribution centers, retailers and customers. In general, the number of firms in each supply chain tier is more than one. Thus, the supply–demand relationship of two adjacent tiers in supply chain is the many-to-many type. Most of supplier selection processes are based on bidding and negotiation mechanism. Due to the diversity of intellectual backgrounds of the negotiating parties, competitive nature, many variables involved in supply–demand relationship, complex interactions and inadequate negotiation knowledge of project participants, the supplier negotiation process is sophisticated. This paper intends to develop the many-to-many supplier negotiation process, design the interactive and competitive bidding strategies of both-side parties, and conduct experimental simulations to investigate how the negotiation efficiency influenced by the varying bidding strategies of a considered demander. The purpose of this paper is to explore the relationships between a demander’s bidding strategies and negotiation efficiency under different order and competitive conditions. The explorative results will be the decision support for assisting a demander to determine the proper bidding strategy in many-to-many supplier negotiation process.     

Combined Negotiations in E-Commerce: Concepts and Architecture

Combined Negotiations are a novel and general type of negotiation, in which the user is interested in many goods or services and consequently engages in many negotiations at the same time. The negotiations are independent of each other, whereas the goods or services are typically interdependent. Using currently available technology for electronic negotiations, the user conducts each negotiation separately, and has the burden of coordinating and reconciling them. The inherent complexity of combined negotiations in B2C as well as B2B e-commerce calls for software support.In our research, we aim to devise a Combined Negotiation Support System (CNSS) to help the user conduct all the negotiations at the same time. The CNSS enables the user to control and monitor the progress of the negotiations, makes sure that the specified dependencies are respected, and applies user-defined strategy rules. We have designed such a CNSS which we call CONSENSUS. The architecture of CONSENSUS relies on workflow technology, negotiating software agents, and rule engine technology. The originality of this architecture lies in the fact that the user of CONSENSUS models the combined negotiation at build time using a workflow that captures the sequencing of the individual negotiations and the dependencies between them. At runtime, software agents are assigned to individual negotiations, and they participate in the combined negotiation as actors in the workflow. The user can monitor the progress of the combined negotiation as a whole, and the progress of individual negotiations via dedicated graphical user interfaces. We rely on rule engine technology to enable the agents to use negotiation strategies.The paper introduces combined negotiations with a usage scenario. Then, combined negotiations are detailed, along with the approach taken to cope with their complexity. Afterwards, we describe the functionality a CNSS should provide, and present the architecture of CONSENSUS, together with a discussion of the underlying concepts and technologies. Furthermore, we report on our prototype implementation of CONSENSUS and illustrate it with an example. A discussion of related and future work concludes the paper.     

基于综合安全保护质量的安全服务协商模型

在基于分布式服务的系统体系中,上层应用系统需要确定下层安全服务能够按照要求提供安全服务功能,根据不断变化的实际需要和当前系统状态自适应地调整服务者和服务要求,并同时保障系统的安全性。本文探讨了在应用系统和安全服务之间对安全保护质量进行协商的协议模型,目标是使应用系统所要求达到的安全水平能获得安全服务的支支持,并能得到下层安全服务的有效遵守,使系统的整体安全水平得到保障。     

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。     

基于改进鲸鱼优化算法的多无人机围捕

无人机围捕是一项具有挑战性和现实意义的任务,为使无人机可以成功有效地围捕移动目标,提出一种基于动态预测围捕点和改进鲸鱼优化算法的多无人机围捕算法。在环境未知,目标运动轨迹未知的情况下,首先利用多项式拟合预测目标运动轨迹,通过动态预测步数得到预测点,在其周围设置围捕点,然后使用双向协商法为无人机合理分配各个目标点。针对鲸鱼优化算法容易陷入局部最优的缺点,提出基于自适应权重和改变螺旋线位置更新的方法,从而提升算法的开发能力和搜索能力。最终在不同实验环境下进行多次实验仿真,实验结果表明了所提出算法的有效性。     

A recursive genetic framework for evolutionary decision-making in problems with high dynamism

Communication and coordination are the main cores for reaching a constructive agreement among multi-agent systems (MASs). Dividing the overall performance of MAS to individual agents may lead to group learning as opposed to individual learning, which is one of the weak points of MASs. This paper proposes a recursive genetic framework for solving problems with high dynamism. In this framework, a combination of genetic algorithm and multi-agent capabilities is utilised to accelerate team learning and accurate credit assignment. The argumentation feature is used to accomplish agent learning and the negotiation features of MASs are used to achieve a credit assignment. The proposed framework is quite general and its recursive hierarchical structure could be extended. We have dedicated one special controlling module for increasing convergence time. Due to the complexity of blackjack, we have applied it as a possible test bed to evaluate the system’s performance. The learning rate of agents is measured as well as their credit assignment. The analysis of the obtained results led us to believe that our robust framework with the proposed negotiation operator is a promising methodology to solve similar problems in other areas with high dynamism.