基于Agent的协商与谈判的研究

随着在线交易的越来越普遍,电子商务己成为当前一种重要的商务方式。在商务活动中,买卖双方在交易时必然会因商品属性的要求不同而发生争议,这时一般采用协商、谈判方式来达成共识。为了达到这种共识提出了多Agent的协商与谈判的技术。首先对限时条件下的两方多议题协商提出了多回合协商框架,其次探讨了时间约束下的谈判策略。     

多agent两阶段多属性协商模型

多议题模型中,在议题的评价机制上买卖双方大多采用多属性效用理论或其变形理论,如距离函数等。提出了多属性效用理论实际应用中的悖论问题,分析了造成理论应用偏差的原因和B2C领域电子商务活动交易的特点,进而提出了一个协商拍卖模型。该模型分两个阶段来进行,提高了协商效率,而且允许卖方体现商品的特色,差异化自己的商品。     

基于对手不完全信息的订单在线智能协商模型

针对订单在线协商延误率和失败率高的问题,基于Zeuthen协商策略提出多阶段多边协进化协商算法。引入新的协调者角色控制多边谈判,并利用贝叶斯原理,通过逐渐修正对手底价估算向量的概率分布和动态调整报价曲线获得最优协商让步幅度,结合同步淘汰机制有效避免了无效协商。实验表明该模型能够充分利用对手信息实时更新智能体协商信念,进而明显地改进了协商行为的效用。     

具备隐形协商空间的服务连续双向拍卖策略研究

针对Web服务市场化交易中,买卖双方单一报价交易成功率低的难点,提出了具备隐形协商空间的服务连续双向拍卖策略,定义了由交易双方的成本、报价形成的买卖方报价协商空间,根据不同的服务供需环境,对议价采用不同的快速协商模式。模拟实验表明,该策略提高了服务交易成功率,又使得服务交易双方由传统单一固定的收益转变为根据市场环境获得区间内可变的收益,客观上有利于指导服务双方根据市场变化调整服务报价。     

基于模糊的电子商务谈判模型及其仿真

主要是从模糊的角度来讨论谈判者对对方报价的接受度,采用隶属值来表示这个接受度.考虑了不同的谈判条款对谈判者的影响权重不同,采用组合的方法来考虑多条款谈判中的让步问题,建立并应用模型解决了一个电子商务谈判的实例,并且用Swarm仿真对此实例进行了仿真研究.结果表明,此模型比传统的谈判模型更加符合现实谈判.     

一种基于协商和Agent的电子交易框架

固定的商品价格已经限制了传统电子商务的发展,文章提出了一种基于协商和Agent的电子交易框架,使用购买代理和销售代理代替现实生活中的买卖双方,来解决企业对消费者(B-C)的零售商务活动。这种多Agent的电子交易框架应用谈判模式提取和移动Agent技术,很好地模拟了现实生活中的商务活动。     

基于团队合作博弈的自动协商模型

企业在联盟方式下运营,有利于提高供应链的协同运作能力.为进一步发挥其在与需求方进行协商时的群体协作报价优势,将协商团队、合作博弈和自动协商理论进行融合,以供应链典型生产和物流企业结成产运纵向联盟为背景,提出基于团队合作博弈的纵向联盟成员协同报价自动协商模型,并基于Shapley值对报价方案按照成员报价的贡献对联盟收益进行分配.研究表明,产运纵向联盟集中定价的情况下,团队成员采取完全合作的方式进行报价,可以在团队收益最大化的同时,实现自身收益最佳,且有利于维持激烈市场竞争环境下产运纵向联盟的稳定合作关系.     

Multi-Agent采购系统的遗传优化研究

本文提出了一种基于Multi-Agent的电子商务谈判协商模型,并利用改进的遗传算法实现了协商Agent交易方案的自动生成。实验表明,上述方法可以对不断优化Multi-Agent的协商效率,高效、快速地生成使参与自动协商的Agent双赢的交易方案。     

一种零售电子市场中的商品交易自动协商模型

本文提出了一个针对零售电子市场中商品交易协商的双边多议题自动协商模型。基于实质利益协商法的原则,综合采用带优先级的模糊约束满足问题(PFCSP)和多属性效用理论(MAUT)的思想对协商进行建模,给出了买卖双方Agent的形式化模型,对协商双方的行为和策略进行了算法描述,并对协商可能获得的结果进行了分析。     

Multi-Agent采购系统的遗传优化研究

本文提出了一种基于Multi-Agent的电子商务谈判协商模型,并利用改进的遗传算法实现了协商Agent交易方案的自动生成.实验表明,上述方法可以对不断优化Multi-Agent的协商效率,高效、快速地生成使参与自动协商的Agent双赢的交易方案.     

Analyzing strategies of mobile agents on malicious cloud platform with Agent-Based Computational Economic Approach

Cloud computing is a service model that enables resource-limited mobile devices to remotely execute tasks from a server. The mobile agent is a software program installed in the mobile device to negotiate a diversity of commerce transactions with other mobile agents in the cloud. However, the negotiation plans carried by mobile agents are easily be eavesdropped on by the malicious cloud platforms, since the codes of mobile agents are read and executed by the cloud platform. Thus, sellers can tailor the negotiation plans to cheat buyers for seizing buyers’ profits in negotiations after eavesdropping on buyers’ negotiation plans. In this paper, we consider the buyers can take actions to resist the sellers’ cheatings, which is that the buyers can tailor their plans with extremely low demand to decrease sellers’ profits before migrate to the hosts. In this paper, we consider actions that buyers can take to resist sellers’ cheatings, that is the buyers can tailor their plans with extremely low demands before migrate to the cloud platform. Above situations between buyers and sellers are modeled as a mathematical model called Eavesdropping and Resistance of Negotiation (ERN) Game. The strategies of the buyers and sellers playing the ERN Game are analyzed by the Agent-Based Computational Economic approach. The simulation results show the cooperative strategies will be emerged between buyers and sellers in the ERN Game.     

PLAMUN: a platform for multi-user negotiation

This paper presents a web-based negotiation support system (NSS) that allows multiple buyers and multiple sellers to negotiate on a common platform. It has been implemented on the Indian Institute of Technology, Kharagpur intranet to help students to trade old books and bicycles online. In this paper we discuss the architecture and the key features of the system. The system guides the participants to modify their perceptions and preferences and to make offers during the negotiation process. The suggestions are generated by the negotiation system so as to find buyer-seller allocations that benefit all parties in the trade and maximize the utility of the system.     

FOXPRO与其它工具的接口研究

本文针对目前前流行的ＦＯＸＰＲＯ数据库，讨论了其接口问题。     

Dynamic Pricing on the Internet: Theory and Simulations

As is the case with traditional markets, the sellers on the Internet do not usually know the demand functions of their customers. However, in such a digital environment, a seller can experiment different prices in order to maximize his profits. In this paper, we fit the dynamic pricing model of Rothschild (1974) to match the pricing problem of a Web-store. In this setting, we define the optimization problem of a Web-store and by simulations we study the price dynamics that can appear when all the sellers on a given market follow an optimal pricing policy.     

Evaluating the trustworthiness of advice about seller agents in e-marketplaces: A personalized approach

In this paper, we present a model for evaluating the trustworthiness of advice about seller agents in electronic marketplaces. In particular, we propose a novel personalized approach for effectively handling unfair ratings of sellers provided to buyer agents from other buyers (called advisors). Our approach offers flexibility for buyers to weight their value for private and public knowledge about advisors. A personalized approach is proposed as well for buyers to model the trustworthiness of sellers, based on the advice provided. Experimental results demonstrate that our approach can effectively model trustworthiness for both advisors and sellers, even when there are large numbers of unfair ratings.     

Numerical study of an optimal control problem for a catalytic converter function

We introduce a model of a catalytic converter, and we study mathematical properties of a control function and a cost function as an optimal control problem. We give a finite-difference scheme for solving various cost functions and discuss the results.     

控讨基于教学过程的ITS系统模型

计算机智能导师系统（ＩＴＳ）,是智能化教学的一个分支。围绕教师在常规教学过程中所起的作用,分析研究了教师在教学的各个阶段中所承担的角色和完成的工作,并根据教学过程的动态特性,提出了一个基于教学过程的ＩＴＳ模型。还针对ＩＴＳ模型中的学科知识库、学生模型和教学策略推理机的实现方法给出了描述。     

COMBINING TRUST MODELING AND MECHANISM DESIGN FOR PROMOTING HONESTY IN E‐MARKETPLACES

In this paper, we propose a novel incentive mechanism for promoting honesty in electronic marketplaces that is based on trust modeling. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neighbors to form a social network which can be used to ask advice about sellers. In addition, however, sellers model the reputation of buyers based on the social network. Reputable buyers provide truthful ratings for sellers, and are likely to be neighbors of many other buyers. Sellers will provide more attractive products to reputable buyer to build their own reputation. We theoretically prove that a marketplace operating with our mechanism leads to greater profit both for honest buyers and honest sellers. We emphasize the value of our approach through a series of illustrative examples and in direct contrast to other frameworks for addressing agent trustworthiness. In all, we offer an effective approach for the design of e‐marketplaces that is attractive to users, through its promotion of honesty.     

Analyses of sensitivity and competition of an EOQ model

Min (1992) proposed a symmetric Cournot-like oligopoly model consisting of sellers who are profit maximizing EOQ-based decision makers. In this paper Min's 1992 model is extended by means of sensitivity and equilibrium analyses. The primary objective of such extensions is to enhance the general understanding of relationships between critical quantities in inventory theory (such as the order quantity) and in microeconomic theory (such as the number of competing sellers). In particular, for EOQ-based decision making sellers, the effects on each seller's profitability caused by the entry of an additional competitor, as well as the maximum number of competitors the entire market can sustain, are examined.     

一种激励学习遗忘算法

提出了一种基于动态规划方法的激励学习遗忘算法,这个算法是通过将记忆心理学中有关遗忘的基本原理引入到值函数的激励学习中,导出了一类用动态规划方法解决激励学习问题的比较好的方法,即Forget-DP算法。